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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "markdown", |
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| 5 | "metadata": {}, |
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| 6 | "source": [ |
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| 7 | "# Preparing the dataset for hippocampus segmentation\n", |
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| 8 | "\n", |
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| 9 | "In this notebook you will use the skills and methods that we have talked about during our EDA Lesson to prepare the hippocampus dataset using Python. Follow the Notebook, writing snippets of code where directed so using Task comments, similar to the one below, which expects you to put the proper imports in place. Write your code directly in the cell with TASK comment. Feel free to add cells as you see fit, but please make sure that code that performs that tasked activity sits in the same cell as the Task comment.\n" |
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| 10 | ] |
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| 11 | }, |
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| 12 | { |
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| 13 | "cell_type": "code", |
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| 14 | "execution_count": 1, |
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| 15 | "metadata": {}, |
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| 16 | "outputs": [], |
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| 17 | "source": [ |
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| 18 | "# TASK: Import the following libraries that we will use: nibabel, matplotlib, numpy\n", |
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| 19 | "\n", |
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| 20 | "import matplotlib.pyplot as plt\n", |
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| 21 | "import nibabel as nib\n", |
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| 22 | "import numpy as np\n", |
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| 23 | "import os\n", |
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| 24 | "from PIL import Image\n", |
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| 25 | "import glob\n", |
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| 26 | "import shutil\n" |
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| 27 | ] |
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| 28 | }, |
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| 29 | { |
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| 30 | "cell_type": "markdown", |
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| 31 | "metadata": {}, |
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| 32 | "source": [ |
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| 33 | "It will help your understanding of the data a lot if you were able to use a tool that allows you to view NIFTI volumes, like [3D Slicer](https://www.slicer.org/). I will refer to Slicer throughout this Notebook and will be pasting some images showing what your output might look like." |
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| 34 | ] |
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| 35 | }, |
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| 36 | { |
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| 37 | "cell_type": "markdown", |
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| 38 | "metadata": {}, |
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| 39 | "source": [ |
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| 40 | "## Loading NIFTI images using NiBabel\n", |
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| 41 | "\n", |
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| 42 | "NiBabel is a python library for working with neuro-imaging formats (including NIFTI) that we have used in some of the exercises throughout the course. Our volumes and labels are in NIFTI format, so we will use nibabel to load and inspect them.\n", |
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| 43 | "\n", |
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| 44 | "NiBabel documentation could be found here: https://nipy.org/nibabel/\n", |
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| 45 | "\n", |
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| 46 | "Our dataset sits in two directories - *images* and *labels*. Each image is represented by a single file (we are fortunate to have our data converted to NIFTI) and has a corresponding label file which is named the same as the image file.\n", |
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| 47 | "\n", |
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| 48 | "Note that our dataset is \"dirty\". There are a few images and labels that are not quite right. They should be quite obvious to notice, though. The dataset contains an equal amount of \"correct\" volumes and corresponding labels, and you don't need to alter values of any samples in order to get the clean dataset." |
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| 49 | ] |
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| 50 | }, |
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| 51 | { |
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| 52 | "cell_type": "code", |
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| 53 | "execution_count": 2, |
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| 54 | "metadata": {}, |
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| 55 | "outputs": [ |
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| 56 | { |
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| 57 | "name": "stdout", |
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| 58 | "output_type": "stream", |
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| 59 | "text": [ |
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| 60 | "\u001b[0m\u001b[01;31mhippocampus_001.nii.gz\u001b[0m \u001b[01;31mhippocampus_146.nii.gz\u001b[0m \u001b[01;31mhippocampus_277.nii.gz\u001b[0m\r\n", |
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| 62 | "\u001b[01;31mhippocampus_004.nii.gz\u001b[0m \u001b[01;31mhippocampus_149.nii.gz\u001b[0m \u001b[01;31mhippocampus_280.nii.gz\u001b[0m\r\n", |
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| 63 | "\u001b[01;31mhippocampus_006.nii.gz\u001b[0m \u001b[01;31mhippocampus_150.nii.gz\u001b[0m \u001b[01;31mhippocampus_281.nii.gz\u001b[0m\r\n", |
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| 64 | "\u001b[01;31mhippocampus_007.nii.gz\u001b[0m \u001b[01;31mhippocampus_152.nii.gz\u001b[0m \u001b[01;31mhippocampus_282.nii.gz\u001b[0m\r\n", |
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| 88 | "\u001b[01;31mhippocampus_045.nii.gz\u001b[0m \u001b[01;31mhippocampus_181.nii.gz\u001b[0m \u001b[01;31mhippocampus_316.nii.gz\u001b[0m\r\n", |
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| 93 | "\u001b[01;31mhippocampus_051.nii.gz\u001b[0m \u001b[01;31mhippocampus_190.nii.gz\u001b[0m \u001b[01;31mhippocampus_321.nii.gz\u001b[0m\r\n", |
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| 134 | "\u001b[01;31mhippocampus_125.nii.gz\u001b[0m \u001b[01;31mhippocampus_251.nii.gz\u001b[0m \u001b[01;31mhippocampus_376.nii.gz\u001b[0m\r\n", |
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| 135 | "\u001b[01;31mhippocampus_126.nii.gz\u001b[0m \u001b[01;31mhippocampus_252.nii.gz\u001b[0m \u001b[01;31mhippocampus_378.nii.gz\u001b[0m\r\n", |
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| 136 | "\u001b[01;31mhippocampus_127.nii.gz\u001b[0m \u001b[01;31mhippocampus_253.nii.gz\u001b[0m \u001b[01;31mhippocampus_380.nii.gz\u001b[0m\r\n", |
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| 137 | "\u001b[01;31mhippocampus_130.nii.gz\u001b[0m \u001b[01;31mhippocampus_257.nii.gz\u001b[0m \u001b[01;31mhippocampus_381.nii.gz\u001b[0m\r\n", |
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| 138 | "\u001b[01;31mhippocampus_132.nii.gz\u001b[0m \u001b[01;31mhippocampus_259.nii.gz\u001b[0m \u001b[01;31mhippocampus_383.nii.gz\u001b[0m\r\n", |
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| 139 | "\u001b[01;31mhippocampus_133.nii.gz\u001b[0m \u001b[01;31mhippocampus_260.nii.gz\u001b[0m \u001b[01;31mhippocampus_385.nii.gz\u001b[0m\r\n", |
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| 140 | "\u001b[01;31mhippocampus_135.nii.gz\u001b[0m \u001b[01;31mhippocampus_261.nii.gz\u001b[0m \u001b[01;31mhippocampus_386.nii.gz\u001b[0m\r\n", |
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| 141 | "\u001b[01;31mhippocampus_136.nii.gz\u001b[0m \u001b[01;31mhippocampus_263.nii.gz\u001b[0m \u001b[01;31mhippocampus_387.nii.gz\u001b[0m\r\n", |
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| 142 | "\u001b[01;31mhippocampus_138.nii.gz\u001b[0m \u001b[01;31mhippocampus_264.nii.gz\u001b[0m \u001b[01;31mhippocampus_389.nii.gz\u001b[0m\r\n", |
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| 143 | "\u001b[01;31mhippocampus_141.nii.gz\u001b[0m \u001b[01;31mhippocampus_265.nii.gz\u001b[0m \u001b[01;31mhippocampus_390.nii.gz\u001b[0m\r\n", |
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| 144 | "\u001b[01;31mhippocampus_142.nii.gz\u001b[0m \u001b[01;31mhippocampus_268.nii.gz\u001b[0m \u001b[01;31mhippocampus_393.nii.gz\u001b[0m\r\n", |
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| 145 | "\u001b[01;31mhippocampus_143.nii.gz\u001b[0m \u001b[01;31mhippocampus_269.nii.gz\u001b[0m \u001b[01;31mhippocampus_394.nii.gz\u001b[0m\r\n", |
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| 146 | "\u001b[01;31mhippocampus_144.nii.gz\u001b[0m \u001b[01;31mhippocampus_274.nii.gz\u001b[0m\r\n", |
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| 147 | "\u001b[01;31mhippocampus_145.nii.gz\u001b[0m \u001b[01;31mhippocampus_276.nii.gz\u001b[0m\r\n" |
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| 148 | ] |
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| 149 | } |
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| 150 | ], |
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| 151 | "source": [ |
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| 152 | "ls /data/TrainingSet/labels" |
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| 153 | ] |
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| 154 | }, |
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| 155 | { |
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| 156 | "cell_type": "code", |
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| 157 | "execution_count": 3, |
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| 158 | "metadata": {}, |
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| 159 | "outputs": [ |
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| 160 | { |
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| 161 | "name": "stdout", |
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| 162 | "output_type": "stream", |
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| 163 | "text": [ |
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| 164 | "\u001b[0m\u001b[01;31mhippocampus_001.nii.gz\u001b[0m \u001b[01;31mhippocampus_145.nii.gz\u001b[0m \u001b[01;31mhippocampus_276.nii.gz\u001b[0m\r\n", |
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| 165 | "\u001b[01;31mhippocampus_003.nii.gz\u001b[0m \u001b[01;31mhippocampus_146.nii.gz\u001b[0m \u001b[01;31mhippocampus_277.nii.gz\u001b[0m\r\n", |
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| 166 | "\u001b[01;31mhippocampus_004.nii.gz\u001b[0m \u001b[01;31mhippocampus_148.nii.gz\u001b[0m \u001b[01;31mhippocampus_279.nii.gz\u001b[0m\r\n", |
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| 167 | "\u001b[01;31mhippocampus_006.nii.gz\u001b[0m \u001b[01;31mhippocampus_149.nii.gz\u001b[0m \u001b[01;31mhippocampus_280.nii.gz\u001b[0m\r\n", |
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| 168 | "\u001b[01;31mhippocampus_007.nii.gz\u001b[0m \u001b[01;31mhippocampus_150.nii.gz\u001b[0m \u001b[01;31mhippocampus_281.nii.gz\u001b[0m\r\n", |
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| 169 | "\u001b[01;31mhippocampus_008.nii.gz\u001b[0m \u001b[01;31mhippocampus_152.nii.gz\u001b[0m \u001b[01;31mhippocampus_282.nii.gz\u001b[0m\r\n", |
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| 170 | "\u001b[01;31mhippocampus_010.nii.gz\u001b[0m \u001b[01;31mhippocampus_154.nii.gz\u001b[0m \u001b[01;31mhippocampus_286.nii.gz\u001b[0m\r\n", |
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| 171 | "\u001b[01;31mhippocampus_011.nii.gz\u001b[0m \u001b[01;31mhippocampus_155.nii.gz\u001b[0m \u001b[01;31mhippocampus_287.nii.gz\u001b[0m\r\n", |
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| 172 | "\u001b[01;31mhippocampus_014.nii.gz\u001b[0m \u001b[01;31mhippocampus_156.nii.gz\u001b[0m \u001b[01;31mhippocampus_288.nii.gz\u001b[0m\r\n", |
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| 173 | "\u001b[01;31mhippocampus_015.nii.gz\u001b[0m \u001b[01;31mhippocampus_157.nii.gz\u001b[0m \u001b[01;31mhippocampus_289.nii.gz\u001b[0m\r\n", |
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| 174 | "\u001b[01;31mhippocampus_017.nii.gz\u001b[0m \u001b[01;31mhippocampus_158.nii.gz\u001b[0m \u001b[01;31mhippocampus_290.nii.gz\u001b[0m\r\n", |
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| 175 | "\u001b[01;31mhippocampus_019.nii.gz\u001b[0m \u001b[01;31mhippocampus_160.nii.gz\u001b[0m \u001b[01;31mhippocampus_292.nii.gz\u001b[0m\r\n", |
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| 176 | "\u001b[01;31mhippocampus_020.nii.gz\u001b[0m \u001b[01;31mhippocampus_161.nii.gz\u001b[0m \u001b[01;31mhippocampus_294.nii.gz\u001b[0m\r\n", |
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| 177 | "\u001b[01;31mhippocampus_023.nii.gz\u001b[0m \u001b[01;31mhippocampus_162.nii.gz\u001b[0m \u001b[01;31mhippocampus_295.nii.gz\u001b[0m\r\n", |
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| 178 | "\u001b[01;31mhippocampus_024.nii.gz\u001b[0m \u001b[01;31mhippocampus_163.nii.gz\u001b[0m \u001b[01;31mhippocampus_296.nii.gz\u001b[0m\r\n", |
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| 179 | "\u001b[01;31mhippocampus_025.nii.gz\u001b[0m \u001b[01;31mhippocampus_164.nii.gz\u001b[0m \u001b[01;31mhippocampus_297.nii.gz\u001b[0m\r\n", |
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| 180 | "\u001b[01;31mhippocampus_026.nii.gz\u001b[0m \u001b[01;31mhippocampus_165.nii.gz\u001b[0m \u001b[01;31mhippocampus_298.nii.gz\u001b[0m\r\n", |
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| 181 | "\u001b[01;31mhippocampus_033.nii.gz\u001b[0m \u001b[01;31mhippocampus_166.nii.gz\u001b[0m \u001b[01;31mhippocampus_299.nii.gz\u001b[0m\r\n", |
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| 182 | "\u001b[01;31mhippocampus_034.nii.gz\u001b[0m \u001b[01;31mhippocampus_169.nii.gz\u001b[0m \u001b[01;31mhippocampus_300.nii.gz\u001b[0m\r\n", |
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| 183 | "\u001b[01;31mhippocampus_035.nii.gz\u001b[0m \u001b[01;31mhippocampus_170.nii.gz\u001b[0m \u001b[01;31mhippocampus_301.nii.gz\u001b[0m\r\n", |
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| 184 | "\u001b[01;31mhippocampus_036.nii.gz\u001b[0m \u001b[01;31mhippocampus_171.nii.gz\u001b[0m \u001b[01;31mhippocampus_302.nii.gz\u001b[0m\r\n", |
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| 185 | "\u001b[01;31mhippocampus_037.nii.gz\u001b[0m \u001b[01;31mhippocampus_172.nii.gz\u001b[0m \u001b[01;31mhippocampus_303.nii.gz\u001b[0m\r\n", |
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| 186 | "\u001b[01;31mhippocampus_038.nii.gz\u001b[0m \u001b[01;31mhippocampus_173.nii.gz\u001b[0m \u001b[01;31mhippocampus_304.nii.gz\u001b[0m\r\n", |
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| 187 | "\u001b[01;31mhippocampus_039.nii.gz\u001b[0m \u001b[01;31mhippocampus_174.nii.gz\u001b[0m \u001b[01;31mhippocampus_305.nii.gz\u001b[0m\r\n", |
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| 188 | "\u001b[01;31mhippocampus_040.nii.gz\u001b[0m \u001b[01;31mhippocampus_175.nii.gz\u001b[0m \u001b[01;31mhippocampus_308.nii.gz\u001b[0m\r\n", |
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| 189 | "\u001b[01;31mhippocampus_041.nii.gz\u001b[0m \u001b[01;31mhippocampus_176.nii.gz\u001b[0m \u001b[01;31mhippocampus_309.nii.gz\u001b[0m\r\n", |
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| 190 | "\u001b[01;31mhippocampus_042.nii.gz\u001b[0m \u001b[01;31mhippocampus_177.nii.gz\u001b[0m \u001b[01;31mhippocampus_310.nii.gz\u001b[0m\r\n", |
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| 191 | "\u001b[01;31mhippocampus_044.nii.gz\u001b[0m \u001b[01;31mhippocampus_178.nii.gz\u001b[0m \u001b[01;31mhippocampus_311.nii.gz\u001b[0m\r\n", |
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| 192 | "\u001b[01;31mhippocampus_045.nii.gz\u001b[0m \u001b[01;31mhippocampus_180.nii.gz\u001b[0m \u001b[01;31mhippocampus_314.nii.gz\u001b[0m\r\n", |
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| 193 | "\u001b[01;31mhippocampus_046.nii.gz\u001b[0m \u001b[01;31mhippocampus_181.nii.gz\u001b[0m \u001b[01;31mhippocampus_316.nii.gz\u001b[0m\r\n", |
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| 194 | "\u001b[01;31mhippocampus_048.nii.gz\u001b[0m \u001b[01;31mhippocampus_184.nii.gz\u001b[0m \u001b[01;31mhippocampus_317.nii.gz\u001b[0m\r\n", |
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| 195 | "\u001b[01;31mhippocampus_049.nii.gz\u001b[0m \u001b[01;31mhippocampus_185.nii.gz\u001b[0m \u001b[01;31mhippocampus_318.nii.gz\u001b[0m\r\n", |
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| 196 | "\u001b[01;31mhippocampus_050.nii.gz\u001b[0m \u001b[01;31mhippocampus_188.nii.gz\u001b[0m \u001b[01;31mhippocampus_319.nii.gz\u001b[0m\r\n", |
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| 197 | "\u001b[01;31mhippocampus_051.nii.gz\u001b[0m \u001b[01;31mhippocampus_189.nii.gz\u001b[0m \u001b[01;31mhippocampus_320.nii.gz\u001b[0m\r\n", |
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| 198 | "\u001b[01;31mhippocampus_052.nii.gz\u001b[0m \u001b[01;31mhippocampus_190.nii.gz\u001b[0m \u001b[01;31mhippocampus_321.nii.gz\u001b[0m\r\n", |
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| 199 | "\u001b[01;31mhippocampus_053.nii.gz\u001b[0m \u001b[01;31mhippocampus_193.nii.gz\u001b[0m \u001b[01;31mhippocampus_322.nii.gz\u001b[0m\r\n", |
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| 200 | "\u001b[01;31mhippocampus_056.nii.gz\u001b[0m \u001b[01;31mhippocampus_194.nii.gz\u001b[0m \u001b[01;31mhippocampus_325.nii.gz\u001b[0m\r\n", |
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| 201 | "\u001b[01;31mhippocampus_057.nii.gz\u001b[0m \u001b[01;31mhippocampus_195.nii.gz\u001b[0m \u001b[01;31mhippocampus_326.nii.gz\u001b[0m\r\n", |
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| 202 | "\u001b[01;31mhippocampus_058.nii.gz\u001b[0m \u001b[01;31mhippocampus_197.nii.gz\u001b[0m \u001b[01;31mhippocampus_327.nii.gz\u001b[0m\r\n", |
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| 203 | "\u001b[01;31mhippocampus_060.nii.gz\u001b[0m \u001b[01;31mhippocampus_199.nii.gz\u001b[0m \u001b[01;31mhippocampus_328.nii.gz\u001b[0m\r\n", |
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| 204 | "\u001b[01;31mhippocampus_064.nii.gz\u001b[0m \u001b[01;31mhippocampus_203.nii.gz\u001b[0m \u001b[01;31mhippocampus_329.nii.gz\u001b[0m\r\n", |
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| 205 | "\u001b[01;31mhippocampus_065.nii.gz\u001b[0m \u001b[01;31mhippocampus_204.nii.gz\u001b[0m \u001b[01;31mhippocampus_330.nii.gz\u001b[0m\r\n", |
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| 206 | "\u001b[01;31mhippocampus_067.nii.gz\u001b[0m \u001b[01;31mhippocampus_205.nii.gz\u001b[0m \u001b[01;31mhippocampus_331.nii.gz\u001b[0m\r\n", |
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| 207 | "\u001b[01;31mhippocampus_068.nii.gz\u001b[0m \u001b[01;31mhippocampus_207.nii.gz\u001b[0m \u001b[01;31mhippocampus_332.nii.gz\u001b[0m\r\n", |
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| 208 | "\u001b[01;31mhippocampus_070.nii.gz\u001b[0m \u001b[01;31mhippocampus_210.nii.gz\u001b[0m \u001b[01;31mhippocampus_333.nii.gz\u001b[0m\r\n", |
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| 209 | "\u001b[01;31mhippocampus_074.nii.gz\u001b[0m \u001b[01;31mhippocampus_212.nii.gz\u001b[0m \u001b[01;31mhippocampus_334.nii.gz\u001b[0m\r\n", |
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| 210 | "\u001b[01;31mhippocampus_075.nii.gz\u001b[0m \u001b[01;31mhippocampus_215.nii.gz\u001b[0m \u001b[01;31mhippocampus_335.nii.gz\u001b[0m\r\n", |
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| 211 | "\u001b[01;31mhippocampus_077.nii.gz\u001b[0m \u001b[01;31mhippocampus_216.nii.gz\u001b[0m \u001b[01;31mhippocampus_336.nii.gz\u001b[0m\r\n", |
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| 212 | "\u001b[01;31mhippocampus_083.nii.gz\u001b[0m \u001b[01;31mhippocampus_217.nii.gz\u001b[0m \u001b[01;31mhippocampus_337.nii.gz\u001b[0m\r\n", |
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| 213 | "\u001b[01;31mhippocampus_084.nii.gz\u001b[0m \u001b[01;31mhippocampus_219.nii.gz\u001b[0m \u001b[01;31mhippocampus_338.nii.gz\u001b[0m\r\n", |
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| 214 | "\u001b[01;31mhippocampus_087.nii.gz\u001b[0m \u001b[01;31mhippocampus_220.nii.gz\u001b[0m \u001b[01;31mhippocampus_340.nii.gz\u001b[0m\r\n", |
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| 215 | "\u001b[01;31mhippocampus_088.nii.gz\u001b[0m \u001b[01;31mhippocampus_221.nii.gz\u001b[0m \u001b[01;31mhippocampus_341.nii.gz\u001b[0m\r\n", |
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| 216 | "\u001b[01;31mhippocampus_089.nii.gz\u001b[0m \u001b[01;31mhippocampus_222.nii.gz\u001b[0m \u001b[01;31mhippocampus_343.nii.gz\u001b[0m\r\n", |
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| 217 | "\u001b[01;31mhippocampus_090.nii.gz\u001b[0m \u001b[01;31mhippocampus_223.nii.gz\u001b[0m \u001b[01;31mhippocampus_345.nii.gz\u001b[0m\r\n", |
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| 218 | "\u001b[01;31mhippocampus_091.nii.gz\u001b[0m \u001b[01;31mhippocampus_224.nii.gz\u001b[0m \u001b[01;31mhippocampus_349.nii.gz\u001b[0m\r\n", |
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| 219 | "\u001b[01;31mhippocampus_092.nii.gz\u001b[0m \u001b[01;31mhippocampus_225.nii.gz\u001b[0m \u001b[01;31mhippocampus_350.nii.gz\u001b[0m\r\n", |
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| 220 | "\u001b[01;31mhippocampus_093.nii.gz\u001b[0m \u001b[01;31mhippocampus_226.nii.gz\u001b[0m \u001b[01;31mhippocampus_351.nii.gz\u001b[0m\r\n", |
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| 221 | "\u001b[01;31mhippocampus_094.nii.gz\u001b[0m \u001b[01;31mhippocampus_227.nii.gz\u001b[0m \u001b[01;31mhippocampus_352.nii.gz\u001b[0m\r\n", |
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| 222 | "\u001b[01;31mhippocampus_095.nii.gz\u001b[0m \u001b[01;31mhippocampus_228.nii.gz\u001b[0m \u001b[01;31mhippocampus_353.nii.gz\u001b[0m\r\n", |
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| 223 | "\u001b[01;31mhippocampus_096.nii.gz\u001b[0m \u001b[01;31mhippocampus_229.nii.gz\u001b[0m \u001b[01;31mhippocampus_354.nii.gz\u001b[0m\r\n", |
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| 224 | "\u001b[01;31mhippocampus_097.nii.gz\u001b[0m \u001b[01;31mhippocampus_230.nii.gz\u001b[0m \u001b[01;31mhippocampus_355.nii.gz\u001b[0m\r\n", |
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| 225 | "\u001b[01;31mhippocampus_098.nii.gz\u001b[0m \u001b[01;31mhippocampus_231.nii.gz\u001b[0m \u001b[01;31mhippocampus_356.nii.gz\u001b[0m\r\n", |
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| 226 | "\u001b[01;31mhippocampus_099.nii.gz\u001b[0m \u001b[01;31mhippocampus_232.nii.gz\u001b[0m \u001b[01;31mhippocampus_358.nii.gz\u001b[0m\r\n", |
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| 227 | "\u001b[01;31mhippocampus_101.nii.gz\u001b[0m \u001b[01;31mhippocampus_233.nii.gz\u001b[0m \u001b[01;31mhippocampus_359.nii.gz\u001b[0m\r\n", |
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| 228 | "\u001b[01;31mhippocampus_102.nii.gz\u001b[0m \u001b[01;31mhippocampus_234.nii.gz\u001b[0m \u001b[01;31mhippocampus_360.nii.gz\u001b[0m\r\n", |
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| 229 | "\u001b[01;31mhippocampus_104.nii.gz\u001b[0m \u001b[01;31mhippocampus_235.nii.gz\u001b[0m \u001b[01;31mhippocampus_361.nii.gz\u001b[0m\r\n", |
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| 230 | "\u001b[01;31mhippocampus_105.nii.gz\u001b[0m \u001b[01;31mhippocampus_236.nii.gz\u001b[0m \u001b[01;31mhippocampus_363.nii.gz\u001b[0m\r\n", |
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| 231 | "\u001b[01;31mhippocampus_106.nii.gz\u001b[0m \u001b[01;31mhippocampus_238.nii.gz\u001b[0m \u001b[01;31mhippocampus_366.nii.gz\u001b[0m\r\n", |
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| 232 | "\u001b[01;31mhippocampus_107.nii.gz\u001b[0m \u001b[01;31mhippocampus_242.nii.gz\u001b[0m \u001b[01;31mhippocampus_367.nii.gz\u001b[0m\r\n", |
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| 233 | "\u001b[01;31mhippocampus_108.nii.gz\u001b[0m \u001b[01;31mhippocampus_243.nii.gz\u001b[0m \u001b[01;31mhippocampus_368.nii.gz\u001b[0m\r\n", |
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| 234 | "\u001b[01;31mhippocampus_109.nii.gz\u001b[0m \u001b[01;31mhippocampus_244.nii.gz\u001b[0m \u001b[01;31mhippocampus_370.nii.gz\u001b[0m\r\n", |
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| 235 | "\u001b[01;31mhippocampus_114.nii.gz\u001b[0m \u001b[01;31mhippocampus_245.nii.gz\u001b[0m \u001b[01;31mhippocampus_372.nii.gz\u001b[0m\r\n", |
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| 236 | "\u001b[01;31mhippocampus_118.nii.gz\u001b[0m \u001b[01;31mhippocampus_248.nii.gz\u001b[0m \u001b[01;31mhippocampus_373.nii.gz\u001b[0m\r\n", |
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| 237 | "\u001b[01;31mhippocampus_123.nii.gz\u001b[0m \u001b[01;31mhippocampus_249.nii.gz\u001b[0m \u001b[01;31mhippocampus_374.nii.gz\u001b[0m\r\n", |
||
| 238 | "\u001b[01;31mhippocampus_124.nii.gz\u001b[0m \u001b[01;31mhippocampus_250.nii.gz\u001b[0m \u001b[01;31mhippocampus_375.nii.gz\u001b[0m\r\n", |
||
| 239 | "\u001b[01;31mhippocampus_125.nii.gz\u001b[0m \u001b[01;31mhippocampus_251.nii.gz\u001b[0m \u001b[01;31mhippocampus_376.nii.gz\u001b[0m\r\n", |
||
| 240 | "\u001b[01;31mhippocampus_126.nii.gz\u001b[0m \u001b[01;31mhippocampus_252.nii.gz\u001b[0m \u001b[01;31mhippocampus_378.nii.gz\u001b[0m\r\n", |
||
| 241 | "\u001b[01;31mhippocampus_127.nii.gz\u001b[0m \u001b[01;31mhippocampus_253.nii.gz\u001b[0m \u001b[01;31mhippocampus_380.nii.gz\u001b[0m\r\n", |
||
| 242 | "\u001b[01;31mhippocampus_130.nii.gz\u001b[0m \u001b[01;31mhippocampus_257.nii.gz\u001b[0m \u001b[01;31mhippocampus_381.nii.gz\u001b[0m\r\n", |
||
| 243 | "\u001b[01;31mhippocampus_132.nii.gz\u001b[0m \u001b[01;31mhippocampus_259.nii.gz\u001b[0m \u001b[01;31mhippocampus_383.nii.gz\u001b[0m\r\n", |
||
| 244 | "\u001b[01;31mhippocampus_133.nii.gz\u001b[0m \u001b[01;31mhippocampus_260.nii.gz\u001b[0m \u001b[01;31mhippocampus_385.nii.gz\u001b[0m\r\n", |
||
| 245 | "\u001b[01;31mhippocampus_135.nii.gz\u001b[0m \u001b[01;31mhippocampus_261.nii.gz\u001b[0m \u001b[01;31mhippocampus_386.nii.gz\u001b[0m\r\n", |
||
| 246 | "\u001b[01;31mhippocampus_136.nii.gz\u001b[0m \u001b[01;31mhippocampus_263.nii.gz\u001b[0m \u001b[01;31mhippocampus_387.nii.gz\u001b[0m\r\n", |
||
| 247 | "\u001b[01;31mhippocampus_138.nii.gz\u001b[0m \u001b[01;31mhippocampus_264.nii.gz\u001b[0m \u001b[01;31mhippocampus_389.nii.gz\u001b[0m\r\n", |
||
| 248 | "\u001b[01;31mhippocampus_141.nii.gz\u001b[0m \u001b[01;31mhippocampus_265.nii.gz\u001b[0m \u001b[01;31mhippocampus_390.nii.gz\u001b[0m\r\n", |
||
| 249 | "\u001b[01;31mhippocampus_142.nii.gz\u001b[0m \u001b[01;31mhippocampus_268.nii.gz\u001b[0m \u001b[01;31mhippocampus_393.nii.gz\u001b[0m\r\n", |
||
| 250 | "\u001b[01;31mhippocampus_143.nii.gz\u001b[0m \u001b[01;31mhippocampus_269.nii.gz\u001b[0m \u001b[01;31mhippocampus_394.nii.gz\u001b[0m\r\n", |
||
| 251 | "\u001b[01;31mhippocampus_144.nii.gz\u001b[0m \u001b[01;31mhippocampus_274.nii.gz\u001b[0m\r\n" |
||
| 252 | ] |
||
| 253 | } |
||
| 254 | ], |
||
| 255 | "source": [ |
||
| 256 | "ls /data/TrainingSet/images" |
||
| 257 | ] |
||
| 258 | }, |
||
| 259 | { |
||
| 260 | "cell_type": "code", |
||
| 261 | "execution_count": 2, |
||
| 262 | "metadata": {}, |
||
| 263 | "outputs": [], |
||
| 264 | "source": [ |
||
| 265 | "# TASK: Your data sits in directory /data/TrainingSet.\n", |
||
| 266 | "# Load an image and a segmentation mask into variables called image and label\n", |
||
| 267 | "\n", |
||
| 268 | "img = nib.load('/data/TrainingSet/images/hippocampus_001.nii.gz')\n", |
||
| 269 | "label = nib.load('/data/TrainingSet/labels/hippocampus_001.nii.gz')" |
||
| 270 | ] |
||
| 271 | }, |
||
| 272 | { |
||
| 273 | "cell_type": "code", |
||
| 274 | "execution_count": 4, |
||
| 275 | "metadata": {}, |
||
| 276 | "outputs": [ |
||
| 277 | { |
||
| 278 | "name": "stdout", |
||
| 279 | "output_type": "stream", |
||
| 280 | "text": [ |
||
| 281 | "img shape is (35, 51, 35)\n", |
||
| 282 | "label shape is (35, 51, 35)\n" |
||
| 283 | ] |
||
| 284 | }, |
||
| 285 | { |
||
| 286 | "data": { |
||
| 287 | "image/png": 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\n", |
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| 288 | "text/plain": [ |
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| 289 | "<Figure size 1080x1080 with 5 Axes>" |
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| 290 | ] |
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| 291 | }, |
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| 292 | "metadata": { |
||
| 293 | "needs_background": "light" |
||
| 294 | }, |
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| 295 | "output_type": "display_data" |
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| 296 | }, |
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| 297 | { |
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| 298 | "data": { |
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| 299 | "image/png": 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GAz5EVf0gjDIjqEw6fT6QslL0CUHc/TrgOoAma9VB1lL2lFkJm0rKbLDqOLa9p56BmSlOXv4iixMdvHrOT7kKknBoaZLBxuVUdaWpf2Yvqa3DTzpSKcohs4npbex+32IOrkpTN62bv5z2WCmaISFSDrmV6BtvcbbXzGZlv2GYBewrZKNEikCZlTBSbo9yaFk9Hzz/Pv5q6oPUWZxaK/9vr4Ok0z3f6ZlrVB1MUtPenJl2IJpCk1lvaaL7Lb3ce+ZXqbcYDbFqQKNaFSg0mZXKMN6DOW4F1mTvrwHWFqY5IkWjzEoYKbdH8Ti0JnqYHq+nIVZD3EIwB6Jl2h0knSAJg81VxGdMJ97SgiXHP9FEmSrvzMbiJGbOIL5iGT1LWpjW0sWMeC0t8TqSVpzCbF+6h3V9cX7YPZPBAzXYUGQPaQ2r8s6sVJx8ptL/TzInSraZ2U7gn4DPAreY2ZXAduDCYjZSZCyUWQkj5bYypOucV06spnruEmpfSdPw+Mukdr5c6maNSxgzG29qYM8Fi+l8Sx9tLa/w5wvvIcbYLsUwVl878Dt89xdvof5lY8HzQ/gBzdJeKmHMrFSefGZrvHSEp95e4LaIFIQyK2Gk3FaGdJXTM8/pmQd1uxLUb2mAnaVu1fiEMbNWW8uBU9LcfdZXmBWvJYYVffT1nvalLFrbR/zBp/F0mrSucVYyYcysVJ6iTwgiIjIZ4k1N+MLZpBtrSBzqw7fuJOjpKXWzRI5k4NmBGi/ugI0MJ5YpxGrMinYYI8DuVDd3981jx1ArW3ZM4/juPoLoztApIgWk4kxEIiE4bj7PXV7PkhW7eO7xORz3bWDjs6VulohUoB93r+BLt55HyzOwaNcgtmNPqZskIiGh4kxEImGgrZbffeOz3LRwPe/kPIZ+0jbuGY9ERCZiY88cZj6QpnbtwwCkS9weEQkPFWciElqxxkaCFQvpn1HL/hMTvLU+MwPy8ua9rF89n5aW1dTu6sE2vUTQ21vi1oqIiIydJauw5YvpW9BIbMipfW5fxV8rMcpUnIlIaNncmTz/h3Wce/oGFtfu5/1NTwD1/GnbPUy/vIttfVNZv/4kjvtaG4H+kImISAjFmhvZdn4rJ793Ey91ttL33dk0bX8ZAo3JRpGKMxEJraC+mhnL9vONuQ9kl9QDsLKqlpVtmxnwIU5ZsgivqyldI0WkvJhmS5RwsepqehcM8cX5t3Fv3yw+Pf0PaSp1o6RoVJyJSGTFiLFq1ss8dd4Kak6b+try+KDTsrGTYMOz+uZRJOpicWInLefgyib6p8ZYsGgX1UWYPv+loW6ufeXNPLJ/Adufn8GyvX0F34dEX3z5Ug6tamOo7vB0rkONVrTcSvlRcSYikZW0OP8w52c8dMVGDqXrXlv+XO9MfnPzG5m9OUnQr+JMJMpiNdW8/LYpnHXpY6xq2M7qmpdosOqC7+eh/nnctvYM5v2ih+VdHdj2XZoIRMYmFmf/6dOYe+ULnD31+dcW18UGi5ZbKT8qzkQkfGJxLGZ4IkZslEOUVlTVsaJq3xHLnqh7lnVTT4F48a5zJMVhAfQHSQZ8iBixol6rajK4GdgwFzzThYoLJx5nYKpzVdvdrKquBorzAbc91UTDDsceeJKgKHuQSjDYbFwy82Euaug46hkVZpVCxZmIhEqssZG+Nx/PgeOT9M5yLpn5eKmbJJOoaVs/16/7Pb4zZzVvWfgC/zDzLuYmGkrdrHFJ1UHnCVOobTvliOVV+7rxLdsJ+vtL1DIRESkVFWciEiqx1ilsf3eMf33HfzIz0cEJVV28OhGIRF/isRc4fnsrQX0tv/7QKi69+CHmJsI5TjHU6Ow/yYilciascWjZXMWUvftBxZmISMVRcSYi4RKPY1MGOa9+Nw2xGsZbmAVJiDU1YjmHNno6TdDXr0lCyljQ1UXQ1YUlEtTuW82Lg9M5oepF6iyezUNhpT2g2wfoDdLsSc8kFRTuhHxPOKkEwOFDGC2AVK1BLNyHa4qIyPioOBORijMjPsSy07bx7CcWEcupw2r3GrPvOkDw9ObSNU7y4oEz9ekBPrf2/Xxmaoq3vmEzX5hzJy3xutE3HoOtqV4+9tKFbNw8D0sbpIxhzhATEREpCBVnIlJxpsfruGHpf9G+KEY656P2/931LrZuX0790yVsnOQnSFN9/yaWPtUAzY3c/ZETaL/gDloKPOD04lALW9Yt4oQbtpNua2b32c10LwhQhSYiIsWg4kxEKk7cYkyP1zP9qA/yS+rb2ZI8vjSNkjELenuht5d4Xz/VB2bwSP98eoNdrz2ftIDZcZ/QaFq/J0n0Qnr3HuJAsruJRJ/hcQgSjuvoQxEpsrQH7Ev3sjed5Om+ucT7NZtrlKk4ExGRUAsGBpj5wCD/kr6YdM3hDy2DrWku/937+Pu2p4kX4OKt3tFJ24ZuGnbX0N8ap2NpjMHmcE5GIiLhsS/dyx+/cDEvPjyf6oPG7Cd7wPW7J6pUnImISKj5wADVv9rAgvuSEDtchKVPXMxt807k79sKc5xqurMTe3wTNRaj9oQl9MxsYbC5IC8tIjKi9iDBiw/P57gvbiHo7MIHB3FdCzGyVJyJiEjo+dAgPjR4xLLEoV7272vmRz0tTI93cWJVF23xevale3hmsJFDQR3zEgdYkYS6WNWwr1sfG6B/qmMrlxHr6cf37ifo6iI2lGaU65+PofEQSxnxfrCUkewNNGNoIaXTJDuN27pO5uX0Vo5P7mdhoq4go6kikyFwI5aGoKc3czi3RJqKMxERiaZ9rzBvbQv/8tSH6V4Q8NFz7+DjLVu5qeMNfPWud1C/M0b/qT3cdPp/sLp6+JdYnuzgnHMf5+7lS+jbOYMlP55C7O7CX/i86qAx9ZkU1QeGSO7vJujuKfg+KpUPDjL73h5+1PU2vtcKS8/dwveW/IQGK/ylF0REJmrUr43MbJ6Z/drMNpnZRjP7WHZ5q5ndZWbPZ/9tKX5zRUanzErYKLPFkX7lALU/fZRZX36YBbenuO/AUgDuO7CEBT8bYvZXHqX2wXq2DrWN+BpzEw18efb9PHb6DVzxe+vpnF+cD/RVndC4YS+x+54gvel5fGCgKPsplDBl1lMp7IENTP/6Qyy6cTtPPTuPXtfIZCUKU26lcuUzpp8Crnb3FcDpwF+a2QnANcA6d18GrMs+FikHymyUDQ4R213D5195E9d3zOS5oUiMMCizxRKk8VSKZOcgT+yYy+cPLGHDjrkkOwfxoRQWQODH/lMYtxjVlqQl0cNAi5FYOJ+hafWkhz8ScnzNrILUjGYS8+cSb5sahotQhyuz7pks9A9QvSfBv+8/k+s6ZrNpUIeIVZhw5VYq0qiHNbr7bmB39n6XmW0C5gAXAG/NrnYjsB74ZFFaKTIGymy0BQcOsmjtNO7c8Ga65sV49AOPce2cB0vdrAlRZosv/uJu5n1nIT+c/g7m7R0i/tJOxjp2srCqnY6VQ1hqLulaGGoq3An5/VOdPWc0EO+vp3nLELUPPEe6s7Ngr19oYc1s0NnJgtt7uPvZ07l9Voz179/Edxeu0/lnFSKsuZXKMqZzzsxsIXAK8BAwIxty3H23mU0veOtEJkiZjZ6gtxf7zRM0/wamnHoij589J/OnNSKU2eJIt7dTdUc7rw52pWHMo1NTYz20zOqko7cFCjlRmkGqzumuc8wh0ZektnqEk+DKUJgy6wMD8OAGmh+E1pXLefKM2QQLnbIfp5SCC1NupbLkXZyZWQPwI+Dj7t5pZvludxVwFUAN478QqMhYKbPRF+sZYN+z07iy5SwW1e3nouZHOS5ZX+pmjZsyWzppD1jXV81th1YB8M4pT/GO2h6SNsLH9vx+NPl79fVCNjt2mDNrvf0MPj+bK2f/Hkk7fM2o4+r3clHT4yxKNpSkXVJ8Yc6tRF9exZmZJcmE+GZ3/3F28V4zm5X9hmEWsG+4bd39OuA6gCZrDdmfHQkrZbYy+M49LLupmhfvWsFDb0iSvjTGP017ptTNGhdltrS6fYC/23wJ8e+3YmlYf/FSfvdN19MS1wewkYQ9s8Hedpb8oIHt9yw/oti+/00nUXfxIB9t2VaKZkmRhT23En35zNZowPXAJnf/Qs5TtwJrsvfXAGsL3zyRsVNmK0fQ1YX/9mmqf/YI054Y4sXekWfdy5cXekQkD8ps6Q15wP7dzUxdv4PWX79E165G+j04Yp10wYfLwisKmQ16e/HHN1J9+yNU/+zwberGNFv7p5a6eVIEoc6tSsGKkc/I2ZnAZcBTZvZEdtmngM8Ct5jZlcB24MLiNFFkzJRZGZcVNbu46TRI1ZxB/d4UdY9uI7132C9QC02ZLQUPaNqR4u8fvYAvTummcVMS7++HVIrmzQnOn38F9VWHL2zdNVDFwX2NeU1zXAEim9m6PQP8+JFTeXTx/DFtt33XVBbsShWpVVIg5Z1bD2jckeZTv30/17Ydem3xKz11NL0IDA2VpFkyufKZrfE+Rj66/u2FbY7IxCmzMl7n1O2k5r03sfXcNq596mwWHpqFTUJxpsyWiDsNv3mJpdum48lapu7bSfpQBx44c9buIPXgFDxe+9rqjS1VDLwhSd+MoPDnnIVMlDOb2PgSx39lFunG5jFtt7yvD9uxZ8yzgMrkKfvcutN43xbqt04nqDmcvzlDaeK7t5Eq8+sfSmGMabZGEZFyNhgkGPAjv1mMERt5UoejtMXreV99N9R3c9+cpRyoW0CyGA2VspFub4f2diBzAaRXpbbtgG07jvgUV79gHp3z59I/DbDsIbAVXqRFUfpQBxzqGPOPNhh9FZFRvfo7KTd/zpG/nyTaVJyJSCTU7Ori8V8v56Rlc19bZuacOf8l/mn2HcxPaOY1mRjv7mHKliGSvUkGG43eWU6qXieCiIhI4ag4E5FoeG4rS7/Zg9fVHF4Wi3HfJW9g86UPMT+hY/VlYtIHO6h58DlqqqtJLZ3NrrPrVZyJiEhBqTgTkUgI+vsJtu04cmEsTu2+09gyOJ3dVc++tjhuRoMlqYtVIZK3IE26sxOARNsU4v31xAZHOPjNwGOOx9ChjyIikjcVZyISXR4wdeMAX1h7Pp9rzjlNvzbNB09+jM/MeJhq01llMg4HOmh7uomBl4f/MxokjO7ZMXpnOZ7Q6JqIiORHxZmIRJc7VfdvZOnGJix2eAL0YEYrP/rEKXxq2m+ojqs4k7FLt79C1QPdVMWHn2zGamqwty6mb3oM119aERHJk/5kiEikBf390N9/xLIEYAcW8shAM9Pj3cNut7unibohzb8mIwjSBL29Iz4dGxoi2RuQ6IuTdvA4BAnP6xDHIAHWUEesvxEfHMQ1fbaISMVQcSYiFSfo7mH23c5fdfyPEQ85a9gGU7bv0vTFMi6eSlH3wkFmpqaQqo9zaHGcvlmemX7/WNsZ9E0z9p81m2TvTBq2dGNPP48PDR57QxERiQQVZyJScYKuLupvf4KGXyQg53DHIwwN6YKfMm6eSpF+/iWqt8SpbZ3CYP0S+mbmt+1AW8Bgi2GpGNBA43NJFWciIhVCxZmIVCQfGNDhYlJcQRoP0jA4hAV5TgqSvbi1x5wYlpntcaQvEEREJHL0G19ERERERKQMqDgTEREREREpAzqsUUREpJjSaao7A2raE5nDFLOCJKTqnaAq55BHh/iAkeg24kNQ1RVAOv361xQRkUhScSYiIlJEQW8vDRt2U7uz6YjjVfpn1NF+cpL+6YeLM3Oo3WNMe7KfRMcA8fZDpPp1bqSISKVQcSYiIlJEnkqR2rYDth25vO64JSSOm/G69as7nKqNO0i3t+tSDiIiFUbFmYiISAlY3wB1ewOwnOE0h9r9KdDU+SIiFUnFmYiISAkE7ftpvd9oqak+Yrl1dpPu7ilRq0REpJRUnImIiJRA0N9PsG1HqZshIiJlRFPpi4iIiIiIlAEVZyIiIiIiImVg1OLMzGrM7GEze9LMNprZ/84ubzWzu8zs+ey/LcVvrsjolFkJG2VWwkaZlTBSbiUM8hk5GwDe5u4nA6uAd5nZ6cA1wDp3Xwasyz4WKQfKrISNMitho8xKGCm3UvZGLc48ozv7MJm9OXABcGN2+Y3A+4rSQpExUmYlbJRZCRtlVsJIuZUwyOucMzOLm+Q6N5kAAAT/SURBVNkTwD7gLnd/CJjh7rsBsv9OH2Hbq8zst2b22yEGCtVukWNSZiVslFkJG2VWwki5lXKXV3Hm7ml3XwXMBVab2Yn57sDdr3P3U9391CTVo28gUgDKrISNMitho8xKGCm3Uu7GNFujux8C1gPvAvaa2SyA7L/7Ct46kQlSZiVslFkJG2VWwki5lXKVz2yN08xsSvZ+LXAOsBm4FViTXW0NsLZYjRQZC2VWwkaZlbBRZiWMlFsJg0Qe68wCbjSzOJli7hZ3v83MHgBuMbMrge3AhUVsp8hYKLMSNsqshI0yK2Gk3ErZG7U4c/cNwCnDLH8FeHsxGiUyEcqshI0yK2GjzEoYKbcSBmM650xERERERESKQ8WZiIiIiIhIGVBxJiIiIiIiUgZUnImIiIiIiJQBFWciIiIiIiJlQMWZiIiIiIhIGVBxJiIiIiIiUgbM3SdvZ2btQA+wf9J2WhptqI/FsMDdp03mDrOZ3Ub0f6ZR7x8os1ET9f5B6fo4qblVZiOlIjIL+kwbIWWX2UktzgDM7Lfufuqk7nSSqY/RE/X+Rr1/UBl9zBX1/ka9f1AZfcwV9f5GvX9QGX3MVQn9jXofy7F/OqxRRERERESkDKg4ExERERERKQOlKM6uK8E+J5v6GD1R72/U+weV0cdcUe9v1PsHldHHXFHvb9T7B5XRx1yV0N+o97Hs+jfp55yJiIiIiIjI6+mwRhERERERkTIwqcWZmb3LzJ41sxfM7JrJ3HcxmNk8M/u1mW0ys41m9rHs8lYzu8vMns/+21Lqtk6UmcXN7HEzuy37OHJ9HE7UMguVk1tlVpkNG2VWmQ0bZVaZDZswZHbSijMziwNfBd4NnABcamYnTNb+iyQFXO3uK4DTgb/M9ukaYJ27LwPWZR+H3ceATTmPo9jHI0Q0s1A5uVVmldmwUWaV2bBRZpXZsCn7zE7myNlq4AV33+Lug8D3gQsmcf8F5+673f2x7P0uMj/sOWT6dWN2tRuB95WmhYVhZnOB9wL/kbM4Un0cQeQyC5WRW2VWmQ0bZVaZDRtlVpkNm7BkdjKLsznAjpzHO7PLIsHMFgKnAA8BM9x9N2TCDkwvXcsK4ovA/wKCnGVR6+NwIp1ZiHRuldkMZTY8lNkMZTY8lNkMZTY8QpHZySzObJhlkZgq0swagB8BH3f3zlK3p5DM7Dxgn7s/Wuq2lEBkMwvRza0y+zrKbJlTZl9HmS1zyuzrKLNlLkyZTUzivnYC83IezwV2TeL+i8LMkmRCfLO7/zi7eK+ZzXL33WY2C9hXuhZO2JnA+Wb2HqAGaDKzm4hWH0cSycxC5HOrzB6mzIaDMnuYMhsOyuxhymw4hCazkzly9giwzMwWmVkVcAlw6yTuv+DMzIDrgU3u/oWcp24F1mTvrwHWTnbbCsXd/9bd57r7QjI/s1+5+x8SoT4eQ+QyC9HPrTKrzIaNMqvMho0yq8yGTZgyO2kjZ+6eMrOPAHcCceBb7r5xsvZfJGcClwFPmdkT2WWfAj4L3GJmVwLbgQtL1L5iinwfI5pZqNzcRr1/ymz0fqZR758yG72fadT7p8xG72dadv0z98gcJisiIiIiIhJak3oRahERERERERmeijMREREREZEyoOJMRERERESkDKg4ExERERERKQMqzkRERERERMqAijMREREREZEyoOJMRERERESkDKg4ExERERERKQP/HwHOBASLiWNHAAAAAElFTkSuQmCC\n", |
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| 300 | "text/plain": [ |
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| 301 | "<Figure size 1080x1080 with 5 Axes>" |
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| 302 | ] |
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| 303 | }, |
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| 304 | "metadata": { |
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| 305 | "needs_background": "light" |
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| 306 | }, |
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| 307 | "output_type": "display_data" |
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| 308 | }, |
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| 309 | { |
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| 310 | "data": { |
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| 311 | "image/png": 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\n", 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| 312 | "text/plain": [ |
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| 313 | "<Figure size 1080x1080 with 5 Axes>" |
||
| 314 | ] |
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| 315 | }, |
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| 316 | "metadata": { |
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| 317 | "needs_background": "light" |
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| 318 | }, |
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| 319 | "output_type": "display_data" |
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| 320 | }, |
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| 321 | { |
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| 322 | "data": { |
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| 323 | "image/png": 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\n", |
||
| 324 | "text/plain": [ |
||
| 325 | "<Figure size 1080x1080 with 5 Axes>" |
||
| 326 | ] |
||
| 327 | }, |
||
| 328 | "metadata": { |
||
| 329 | "needs_background": "light" |
||
| 330 | }, |
||
| 331 | "output_type": "display_data" |
||
| 332 | }, |
||
| 333 | { |
||
| 334 | "data": { |
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| 335 | "image/png": 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\n", |
||
| 336 | "text/plain": [ |
||
| 337 | "<Figure size 1080x1080 with 5 Axes>" |
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| 338 | ] |
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| 339 | }, |
||
| 340 | "metadata": { |
||
| 341 | "needs_background": "light" |
||
| 342 | }, |
||
| 343 | "output_type": "display_data" |
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| 344 | }, |
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| 345 | { |
||
| 346 | "data": { |
||
| 347 | "image/png": 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\n", |
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| 348 | "text/plain": [ |
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| 349 | "<Figure size 1080x1080 with 5 Axes>" |
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| 350 | ] |
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| 351 | }, |
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| 352 | "metadata": { |
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| 353 | "needs_background": "light" |
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| 354 | }, |
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| 355 | "output_type": "display_data" |
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| 356 | } |
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| 357 | ], |
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| 358 | "source": [ |
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| 359 | "# Nibabel can present your image data as a Numpy array by calling the method get_fdata()\n", |
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| 360 | "# The array will contain a multi-dimensional Numpy array with numerical values representing voxel intensities. \n", |
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| 361 | "# In our case, images and labels are 3-dimensional, so get_fdata will return a 3-dimensional array. You can verify this\n", |
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| 362 | "# by accessing the .shape attribute. What are the dimensions of the input arrays?\n", |
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| 363 | "\n", |
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| 364 | "img_np = img.get_fdata()\n", |
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| 365 | "label_np = label.get_fdata()\n", |
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| 366 | "print(f'img shape is {img_np.shape}')\n", |
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| 367 | "print(f'label shape is {label_np.shape}')\n", |
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| 368 | "\n", |
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| 369 | "\n", |
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| 370 | "# TASK: using matplotlib, visualize a few slices from the dataset, along with their labels. \n", |
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| 371 | "# You can adjust plot sizes like so if you find them too small:\n", |
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| 372 | "plt.rcParams[\"figure.figsize\"] = (10,10)\n", |
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| 373 | "\n", |
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| 374 | "fig1, n_ax1 = plt.subplots(1,5,figsize=(15,15))\n", |
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| 375 | "n_ax1 = n_ax1.flatten()\n", |
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| 376 | "\n", |
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| 377 | "for i in range(5):\n", |
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| 378 | " n_ax1[i].imshow(img_np[:,:,(i*7)])\n", |
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| 379 | " n_ax1[i].set_title(f'img Axial slice {i*7}')\n", |
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| 380 | " \n", |
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| 381 | "fig2, n_ax2 = plt.subplots(1,5,figsize=(15,15))\n", |
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| 382 | "n_ax2 = n_ax2.flatten()\n", |
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| 383 | "\n", |
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| 384 | "for i in range(5):\n", |
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| 385 | " n_ax2[i].imshow(label_np[:,:,(i*7)])\n", |
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| 386 | " n_ax2[i].set_title(f'label Axial slice {i*7}')\n", |
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| 387 | " \n", |
||
| 388 | "fig3, n_ax3 = plt.subplots(1,5,figsize=(15,15))\n", |
||
| 389 | "n_ax3 = n_ax3.flatten() \n", |
||
| 390 | "for i in range(5):\n", |
||
| 391 | " n_ax3[i].imshow(img_np[:,i*10,:])\n", |
||
| 392 | " n_ax3[i].set_title(f'img Coronal slice {i*10}')\n", |
||
| 393 | " \n", |
||
| 394 | "fig4, n_ax4 = plt.subplots(1,5,figsize=(15,15))\n", |
||
| 395 | "n_ax4 = n_ax4.flatten()\n", |
||
| 396 | "\n", |
||
| 397 | "for i in range(5):\n", |
||
| 398 | " n_ax4[i].imshow(label_np[:,(i*10),:])\n", |
||
| 399 | " n_ax4[i].set_title(f'label Coronal slice {i*10}')\n", |
||
| 400 | " \n", |
||
| 401 | "fig5, n_ax5 = plt.subplots(1,5,figsize=(15,15))\n", |
||
| 402 | "n_ax5 = n_ax5.flatten() \n", |
||
| 403 | "for i in range(5):\n", |
||
| 404 | " n_ax5[i].imshow(img_np[(i*7),:,:])\n", |
||
| 405 | " n_ax5[i].set_title(f'img Sagital slice {i*7}')\n", |
||
| 406 | " \n", |
||
| 407 | "fig6, n_ax6 = plt.subplots(1,5,figsize=(15,15))\n", |
||
| 408 | "n_ax6 = n_ax6.flatten()\n", |
||
| 409 | "\n", |
||
| 410 | "for i in range(5):\n", |
||
| 411 | " n_ax6[i].imshow(label_np[(i*7),:,:])\n", |
||
| 412 | " n_ax6[i].set_title(f'label Sagital slice {i*7}')" |
||
| 413 | ] |
||
| 414 | }, |
||
| 415 | { |
||
| 416 | "cell_type": "code", |
||
| 417 | "execution_count": 5, |
||
| 418 | "metadata": {}, |
||
| 419 | "outputs": [ |
||
| 420 | { |
||
| 421 | "data": { |
||
| 422 | "text/plain": [ |
||
| 423 | "<matplotlib.image.AxesImage at 0x7f3a75b58b80>" |
||
| 424 | ] |
||
| 425 | }, |
||
| 426 | "execution_count": 5, |
||
| 427 | "metadata": {}, |
||
| 428 | "output_type": "execute_result" |
||
| 429 | }, |
||
| 430 | { |
||
| 431 | "data": { |
||
| 432 | "image/png": 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BYJy4ADBOXAAYJy4AjBMXAMaJCwDjxAWAceICwDhxAWDcNTv5pqr6cJKPJ/l0kk9195GquiHJHyR5WZIPJ/me7v7PvRkTgDW5lDOXb+zuw919ZHl8X5IT3X1rkhPLYwDY1cti9yQ5vtw/nuTe3Y8DwDbYaVw6yZ9X1Xuq6tiy7abuPp0ky+2NezEgAOuzo/dckrymu09V1Y1JHqmqD+70CZYYHUuS6/LCyxgRgLXZ0ZlLd59abs8k+aMktyV5qqoOJclye+YCP3t/dx/p7iMHc+3M1ADsaxeNS1W9qKo+/9n7Sb4lyfuTPJTk6PJtR5M8uFdDArAuO3lZ7KYkf1RVz37/73b3n1XVu5M8UFVvSPKRJK/duzEBWJOLxqW7P5Tkq8+z/aNJ7tiLoQBYN1foAzCuuvvKPVnVvyf5l+ds/uIk/3HFhrhytnG/7NM6bOM+Jdu5X2vfpy/t7hef7wtXNC7nHaDq0XOu+t8a27hf9mkdtnGfku3cr23cp2d5WQyAceICwLj9EJf7Nz3AHtnG/bJP67CN+5Rs535t4z4l2QfvuQCwffbDmQsAW2ajcamqu6vqn6rqiarais+DqaoPV9X7qupkVT266XkuV1W9tarOVNX7z9l2Q1U9UlWPL7fXb3LGS3WBffrpqvq35XidrKpv2+SMl6qqXlpVf1lVj1XVB6rqjcv21R6r59mn1R6rqrquqv62qv5+2aefWbav9jhdzMZeFquqA0n+OcmdSZ5M8u4kr+/uf9zIQEOWT+080t1r/t31VNXXJ3k6yW9196uWbT+f5GPd/eblfwau7+4f3+Scl+IC+/TTSZ7u7l/Y5GyXa1k09lB3v3dZA/A9OfvZSt+flR6r59mn78lKj1WdXT/rRd39dFUdTPKOJG9M8t1Z6XG6mE2eudyW5Inu/lB3fyLJ7+fsB5CxD3T3XyX52HM2r/oD4i6wT6vW3ae7+73L/Y8neSzJS7LiY/U8+7RafdbTy8ODy5/Oio/TxWwyLi9J8q/nPH4yK/8XaHG+D1bbFtv6AXE/XFX/sLxsttqXJarqZUleneRd2ZJj9Zx9SlZ8rKrqQFWdzNmPJ3mku7fmOJ3PJuNS59m2Db+69pru/pok35rkh5aXYti/fjXJy5McTnI6yS9udpzLU1Wfl+RtSX6ku/970/NMOM8+rfpYdfenu/twkluS3FZVr9r0THtpk3F5MslLz3l8S5JTG5plzAU+WG1b7OgD4taku59a/qP/TJJfywqP1/Ia/tuS/E53/+GyedXH6nz7tA3HKkm6+7+SvD3J3Vn5cXo+m4zLu5PcWlVfVlUvSPK6nP0AstV6ng9W2xZb9wFxz/6HvfiurOx4LW8U/0aSx7r7l8750mqP1YX2ac3HqqpeXFVfuNz/3CTfnOSDWfFxupiNXkS5/CrhLyc5kOSt3f1zGxtmQFV9ec6erST//8Fqq9ynqvq9JN+Qs6u2PpXkp5L8cZIHknxJlg+I6+7VvEF+gX36hpx9maWTfDjJDzz7GvgaVNXXJfnrJO9L8pll80/m7HsUqzxWz7NPr89Kj1VVfVXOvmF/IGf/p/6B7v7ZqvqirPQ4XYwr9AEY5wp9AMaJCwDjxAWAceICwDhxAWCcuAAwTlwAGCcuAIz7P/Kts7vca9cDAAAAAElFTkSuQmCC\n", |
||
| 433 | "text/plain": [ |
||
| 434 | "<Figure size 720x720 with 1 Axes>" |
||
| 435 | ] |
||
| 436 | }, |
||
| 437 | "metadata": { |
||
| 438 | "needs_background": "light" |
||
| 439 | }, |
||
| 440 | "output_type": "display_data" |
||
| 441 | } |
||
| 442 | ], |
||
| 443 | "source": [ |
||
| 444 | "plt.imshow(label_np[14,:,:])" |
||
| 445 | ] |
||
| 446 | }, |
||
| 447 | { |
||
| 448 | "cell_type": "code", |
||
| 449 | "execution_count": 6, |
||
| 450 | "metadata": {}, |
||
| 451 | "outputs": [ |
||
| 452 | { |
||
| 453 | "data": { |
||
| 454 | "text/plain": [ |
||
| 455 | "<matplotlib.image.AxesImage at 0x7f3a75b318b0>" |
||
| 456 | ] |
||
| 457 | }, |
||
| 458 | "execution_count": 6, |
||
| 459 | "metadata": {}, |
||
| 460 | "output_type": "execute_result" |
||
| 461 | }, |
||
| 462 | { |
||
| 463 | "data": { |
||
| 464 | "image/png": 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\n", |
||
| 465 | "text/plain": [ |
||
| 466 | "<Figure size 720x720 with 1 Axes>" |
||
| 467 | ] |
||
| 468 | }, |
||
| 469 | "metadata": { |
||
| 470 | "needs_background": "light" |
||
| 471 | }, |
||
| 472 | "output_type": "display_data" |
||
| 473 | } |
||
| 474 | ], |
||
| 475 | "source": [ |
||
| 476 | "plt.imshow((img_np[:,:,14]+label_np[:,:,14]))" |
||
| 477 | ] |
||
| 478 | }, |
||
| 479 | { |
||
| 480 | "cell_type": "markdown", |
||
| 481 | "metadata": {}, |
||
| 482 | "source": [ |
||
| 483 | "Load volume into 3D Slicer to validate that your visualization is correct and get a feel for the shape of structures.Try to get a visualization like the one below (hint: while Slicer documentation is not particularly great, there are plenty of YouTube videos available! Just look it up on YouTube if you are not sure how to do something)\n", |
||
| 484 | "\n", |
||
| 485 | "" |
||
| 486 | ] |
||
| 487 | }, |
||
| 488 | { |
||
| 489 | "cell_type": "code", |
||
| 490 | "execution_count": null, |
||
| 491 | "metadata": {}, |
||
| 492 | "outputs": [], |
||
| 493 | "source": [ |
||
| 494 | "# Stand out suggestion: use one of the simple Volume Rendering algorithms that we've\n", |
||
| 495 | "# implemented in one of our earlier lessons to visualize some of these volumes" |
||
| 496 | ] |
||
| 497 | }, |
||
| 498 | { |
||
| 499 | "cell_type": "code", |
||
| 500 | "execution_count": 65, |
||
| 501 | "metadata": {}, |
||
| 502 | "outputs": [ |
||
| 503 | { |
||
| 504 | "name": "stdout", |
||
| 505 | "output_type": "stream", |
||
| 506 | "text": [ |
||
| 507 | "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 508 | "sizeof_hdr : 348\n", |
||
| 509 | "data_type : b''\n", |
||
| 510 | "db_name : b''\n", |
||
| 511 | "extents : 0\n", |
||
| 512 | "session_error : 0\n", |
||
| 513 | "regular : b'r'\n", |
||
| 514 | "dim_info : 0\n", |
||
| 515 | "dim : [ 3 36 50 31 1 1 1 1]\n", |
||
| 516 | "intent_p1 : 0.0\n", |
||
| 517 | "intent_p2 : 0.0\n", |
||
| 518 | "intent_p3 : 0.0\n", |
||
| 519 | "intent_code : none\n", |
||
| 520 | "datatype : uint8\n", |
||
| 521 | "bitpix : 8\n", |
||
| 522 | "slice_start : 0\n", |
||
| 523 | "pixdim : [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 524 | "vox_offset : 0.0\n", |
||
| 525 | "scl_slope : nan\n", |
||
| 526 | "scl_inter : nan\n", |
||
| 527 | "slice_end : 0\n", |
||
| 528 | "slice_code : unknown\n", |
||
| 529 | "xyzt_units : 10\n", |
||
| 530 | "cal_max : 0.0\n", |
||
| 531 | "cal_min : 0.0\n", |
||
| 532 | "slice_duration : 0.0\n", |
||
| 533 | "toffset : 0.0\n", |
||
| 534 | "glmax : 0\n", |
||
| 535 | "glmin : 0\n", |
||
| 536 | "descrip : b'5.0.10'\n", |
||
| 537 | "aux_file : b'none'\n", |
||
| 538 | "qform_code : scanner\n", |
||
| 539 | "sform_code : scanner\n", |
||
| 540 | "quatern_b : 0.0\n", |
||
| 541 | "quatern_c : 0.0\n", |
||
| 542 | "quatern_d : 0.0\n", |
||
| 543 | "qoffset_x : 1.0\n", |
||
| 544 | "qoffset_y : 1.0\n", |
||
| 545 | "qoffset_z : 1.0\n", |
||
| 546 | "srow_x : [1. 0. 0. 1.]\n", |
||
| 547 | "srow_y : [0. 1. 0. 1.]\n", |
||
| 548 | "srow_z : [0. 0. 1. 1.]\n", |
||
| 549 | "intent_name : b''\n", |
||
| 550 | "magic : b'n+1'\n" |
||
| 551 | ] |
||
| 552 | } |
||
| 553 | ], |
||
| 554 | "source": [ |
||
| 555 | "print(nib.load('/data/TrainingSet/labels/hippocampus_010.nii.gz').header)" |
||
| 556 | ] |
||
| 557 | }, |
||
| 558 | { |
||
| 559 | "cell_type": "markdown", |
||
| 560 | "metadata": {}, |
||
| 561 | "source": [ |
||
| 562 | "## Looking at single image data\n", |
||
| 563 | "In this section we will look closer at the NIFTI representation of our volumes. In order to measure the physical volume of hippocampi, we need to understand the relationship between the sizes of our voxels and the physical world." |
||
| 564 | ] |
||
| 565 | }, |
||
| 566 | { |
||
| 567 | "cell_type": "code", |
||
| 568 | "execution_count": 8, |
||
| 569 | "metadata": { |
||
| 570 | "scrolled": true |
||
| 571 | }, |
||
| 572 | "outputs": [ |
||
| 573 | { |
||
| 574 | "name": "stdout", |
||
| 575 | "output_type": "stream", |
||
| 576 | "text": [ |
||
| 577 | "Img format is <class 'nibabel.nifti1.Nifti1Header'>\n", |
||
| 578 | "Label format is <class 'nibabel.nifti1.Nifti1Header'>\n" |
||
| 579 | ] |
||
| 580 | } |
||
| 581 | ], |
||
| 582 | "source": [ |
||
| 583 | "# Nibabel supports many imaging formats, NIFTI being just one of them. I told you that our images \n", |
||
| 584 | "# are in NIFTI, but you should confirm if this is indeed the format that we are dealing with\n", |
||
| 585 | "# TASK: using .header_class attribute - what is the format of our images?\n", |
||
| 586 | "\n", |
||
| 587 | "print(f'Img format is {img.header_class}')\n", |
||
| 588 | "print(f'Label format is {label.header_class}')" |
||
| 589 | ] |
||
| 590 | }, |
||
| 591 | { |
||
| 592 | "cell_type": "markdown", |
||
| 593 | "metadata": {}, |
||
| 594 | "source": [ |
||
| 595 | "Further down we will be inspecting .header attribute that provides access to NIFTI metadata. You can use this resource as a reference for various fields: https://brainder.org/2012/09/23/the-nifti-file-format/" |
||
| 596 | ] |
||
| 597 | }, |
||
| 598 | { |
||
| 599 | "cell_type": "code", |
||
| 600 | "execution_count": 9, |
||
| 601 | "metadata": {}, |
||
| 602 | "outputs": [ |
||
| 603 | { |
||
| 604 | "name": "stdout", |
||
| 605 | "output_type": "stream", |
||
| 606 | "text": [ |
||
| 607 | "Img: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 608 | "sizeof_hdr : 348\n", |
||
| 609 | "data_type : b''\n", |
||
| 610 | "db_name : b''\n", |
||
| 611 | "extents : 0\n", |
||
| 612 | "session_error : 0\n", |
||
| 613 | "regular : b'r'\n", |
||
| 614 | "dim_info : 0\n", |
||
| 615 | "dim : [ 3 35 51 35 1 1 1 1]\n", |
||
| 616 | "intent_p1 : 0.0\n", |
||
| 617 | "intent_p2 : 0.0\n", |
||
| 618 | "intent_p3 : 0.0\n", |
||
| 619 | "intent_code : none\n", |
||
| 620 | "datatype : uint8\n", |
||
| 621 | "bitpix : 8\n", |
||
| 622 | "slice_start : 0\n", |
||
| 623 | "pixdim : [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 624 | "vox_offset : 0.0\n", |
||
| 625 | "scl_slope : nan\n", |
||
| 626 | "scl_inter : nan\n", |
||
| 627 | "slice_end : 0\n", |
||
| 628 | "slice_code : unknown\n", |
||
| 629 | "xyzt_units : 10\n", |
||
| 630 | "cal_max : 0.0\n", |
||
| 631 | "cal_min : 0.0\n", |
||
| 632 | "slice_duration : 0.0\n", |
||
| 633 | "toffset : 0.0\n", |
||
| 634 | "glmax : 0\n", |
||
| 635 | "glmin : 0\n", |
||
| 636 | "descrip : b'5.0.10'\n", |
||
| 637 | "aux_file : b'none'\n", |
||
| 638 | "qform_code : scanner\n", |
||
| 639 | "sform_code : scanner\n", |
||
| 640 | "quatern_b : 0.0\n", |
||
| 641 | "quatern_c : 0.0\n", |
||
| 642 | "quatern_d : 0.0\n", |
||
| 643 | "qoffset_x : 1.0\n", |
||
| 644 | "qoffset_y : 1.0\n", |
||
| 645 | "qoffset_z : 1.0\n", |
||
| 646 | "srow_x : [1. 0. 0. 1.]\n", |
||
| 647 | "srow_y : [0. 1. 0. 1.]\n", |
||
| 648 | "srow_z : [0. 0. 1. 1.]\n", |
||
| 649 | "intent_name : b''\n", |
||
| 650 | "magic : b'n+1' \n", |
||
| 651 | "\n", |
||
| 652 | "Label: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 653 | "sizeof_hdr : 348\n", |
||
| 654 | "data_type : b''\n", |
||
| 655 | "db_name : b''\n", |
||
| 656 | "extents : 0\n", |
||
| 657 | "session_error : 0\n", |
||
| 658 | "regular : b'r'\n", |
||
| 659 | "dim_info : 0\n", |
||
| 660 | "dim : [ 3 35 51 35 1 1 1 1]\n", |
||
| 661 | "intent_p1 : 0.0\n", |
||
| 662 | "intent_p2 : 0.0\n", |
||
| 663 | "intent_p3 : 0.0\n", |
||
| 664 | "intent_code : none\n", |
||
| 665 | "datatype : uint8\n", |
||
| 666 | "bitpix : 8\n", |
||
| 667 | "slice_start : 0\n", |
||
| 668 | "pixdim : [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 669 | "vox_offset : 0.0\n", |
||
| 670 | "scl_slope : nan\n", |
||
| 671 | "scl_inter : nan\n", |
||
| 672 | "slice_end : 0\n", |
||
| 673 | "slice_code : unknown\n", |
||
| 674 | "xyzt_units : 10\n", |
||
| 675 | "cal_max : 0.0\n", |
||
| 676 | "cal_min : 0.0\n", |
||
| 677 | "slice_duration : 0.0\n", |
||
| 678 | "toffset : 0.0\n", |
||
| 679 | "glmax : 0\n", |
||
| 680 | "glmin : 0\n", |
||
| 681 | "descrip : b'5.0.10'\n", |
||
| 682 | "aux_file : b'none'\n", |
||
| 683 | "qform_code : scanner\n", |
||
| 684 | "sform_code : scanner\n", |
||
| 685 | "quatern_b : 0.0\n", |
||
| 686 | "quatern_c : 0.0\n", |
||
| 687 | "quatern_d : 0.0\n", |
||
| 688 | "qoffset_x : 1.0\n", |
||
| 689 | "qoffset_y : 1.0\n", |
||
| 690 | "qoffset_z : 1.0\n", |
||
| 691 | "srow_x : [1. 0. 0. 1.]\n", |
||
| 692 | "srow_y : [0. 1. 0. 1.]\n", |
||
| 693 | "srow_z : [0. 0. 1. 1.]\n", |
||
| 694 | "intent_name : b''\n", |
||
| 695 | "magic : b'n+1'\n" |
||
| 696 | ] |
||
| 697 | } |
||
| 698 | ], |
||
| 699 | "source": [ |
||
| 700 | "# TASK: How many bits per pixel are used?\n", |
||
| 701 | "print(f'Img: {img.header} \\n')\n", |
||
| 702 | "print(f'Label: {label.header}')" |
||
| 703 | ] |
||
| 704 | }, |
||
| 705 | { |
||
| 706 | "cell_type": "markdown", |
||
| 707 | "metadata": {}, |
||
| 708 | "source": [ |
||
| 709 | "#### Bits per voxel (pixel) is 8." |
||
| 710 | ] |
||
| 711 | }, |
||
| 712 | { |
||
| 713 | "cell_type": "code", |
||
| 714 | "execution_count": 10, |
||
| 715 | "metadata": {}, |
||
| 716 | "outputs": [ |
||
| 717 | { |
||
| 718 | "data": { |
||
| 719 | "text/plain": [ |
||
| 720 | "'\\nxyzt_units indicate the unit of measurements for dim. \\nFrom the Header, xyzt_units in binary is 10, translating to 2. \\n2 translates to NIFTI_UNITS_MM - millimeter.\\n'" |
||
| 721 | ] |
||
| 722 | }, |
||
| 723 | "execution_count": 10, |
||
| 724 | "metadata": {}, |
||
| 725 | "output_type": "execute_result" |
||
| 726 | } |
||
| 727 | ], |
||
| 728 | "source": [ |
||
| 729 | "# TASK: What are the units of measurement?\n", |
||
| 730 | "\n", |
||
| 731 | "'''\n", |
||
| 732 | "xyzt_units indicate the unit of measurements for dim. \n", |
||
| 733 | "From the Header, xyzt_units in binary is 10, translating to 2. \n", |
||
| 734 | "2 translates to NIFTI_UNITS_MM - millimeter.\n", |
||
| 735 | "'''" |
||
| 736 | ] |
||
| 737 | }, |
||
| 738 | { |
||
| 739 | "cell_type": "code", |
||
| 740 | "execution_count": null, |
||
| 741 | "metadata": {}, |
||
| 742 | "outputs": [], |
||
| 743 | "source": [ |
||
| 744 | "# TASK: Do we have a regular grid? What are grid spacings?\n", |
||
| 745 | "\n", |
||
| 746 | "'''\n", |
||
| 747 | "pixdim is grid spacings.\n", |
||
| 748 | "pixdim = [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 749 | "pixdim[1], pixdim[2], pixdim[3] = 1.,1.,1.\n", |
||
| 750 | "\n", |
||
| 751 | "'''" |
||
| 752 | ] |
||
| 753 | }, |
||
| 754 | { |
||
| 755 | "cell_type": "code", |
||
| 756 | "execution_count": null, |
||
| 757 | "metadata": {}, |
||
| 758 | "outputs": [], |
||
| 759 | "source": [ |
||
| 760 | "# TASK: What dimensions represent axial, sagittal, and coronal slices? How do you know?\n", |
||
| 761 | "'''\n", |
||
| 762 | "sform_code = scanner\n", |
||
| 763 | "srow_x, srow_y, srow_z are given.\n", |
||
| 764 | "srow_x = [1. 0 0 1.]\n", |
||
| 765 | "srow_y = [0 1. 0 1.]\n", |
||
| 766 | "srow_z = [0 0 . 1.]\n", |
||
| 767 | "\n", |
||
| 768 | "From NIFITI documentation 3D IMAGE (VOLUME) ORIENTATION AND LOCATION IN SPACE section:\n", |
||
| 769 | "In sform_code method, the (x,y,z) axes refer to a subject-based coordinate system,\n", |
||
| 770 | "with +x = Right +y = Anterior +z = Superior.\n", |
||
| 771 | "The srow_x, _y, _z vectors show that they translate to orthoganal i, j, k vectors.\n", |
||
| 772 | "\n", |
||
| 773 | "Hence, x dimension is sagital (medial and lateral/ left and right since this is the right side of the brain)\n", |
||
| 774 | "y dimension is coronal (anterior and posterior)\n", |
||
| 775 | "z dimension is axial (superior and inferior)\n", |
||
| 776 | "'''" |
||
| 777 | ] |
||
| 778 | }, |
||
| 779 | { |
||
| 780 | "cell_type": "code", |
||
| 781 | "execution_count": 11, |
||
| 782 | "metadata": {}, |
||
| 783 | "outputs": [ |
||
| 784 | { |
||
| 785 | "data": { |
||
| 786 | "text/plain": [ |
||
| 787 | "array([2., 2., 2., ..., 1., 1., 1.])" |
||
| 788 | ] |
||
| 789 | }, |
||
| 790 | "execution_count": 11, |
||
| 791 | "metadata": {}, |
||
| 792 | "output_type": "execute_result" |
||
| 793 | } |
||
| 794 | ], |
||
| 795 | "source": [ |
||
| 796 | "label_np[label_np > 0]" |
||
| 797 | ] |
||
| 798 | }, |
||
| 799 | { |
||
| 800 | "cell_type": "code", |
||
| 801 | "execution_count": 14, |
||
| 802 | "metadata": {}, |
||
| 803 | "outputs": [ |
||
| 804 | { |
||
| 805 | "name": "stdout", |
||
| 806 | "output_type": "stream", |
||
| 807 | "text": [ |
||
| 808 | "Volume of hippocampus label is 2948 mm^3\n" |
||
| 809 | ] |
||
| 810 | } |
||
| 811 | ], |
||
| 812 | "source": [ |
||
| 813 | "# By now you should have enough information to decide what are dimensions of a single voxel\n", |
||
| 814 | "# TASK: Compute the volume (in mm³) of a hippocampus using one of the labels you've loaded. \n", |
||
| 815 | "# You should get a number between ~2200 and ~4500\n", |
||
| 816 | "\n", |
||
| 817 | "'''\n", |
||
| 818 | "One voxel = pixdim[1] * pixdim[2] * pixdim[3] = 1.0 mm^3\n", |
||
| 819 | "'''\n", |
||
| 820 | "\n", |
||
| 821 | "print(f'Volume of hippocampus label is {np.count_nonzero(label_np > 0)} mm^3')" |
||
| 822 | ] |
||
| 823 | }, |
||
| 824 | { |
||
| 825 | "cell_type": "code", |
||
| 826 | "execution_count": 25, |
||
| 827 | "metadata": {}, |
||
| 828 | "outputs": [ |
||
| 829 | { |
||
| 830 | "name": "stdout", |
||
| 831 | "output_type": "stream", |
||
| 832 | "text": [ |
||
| 833 | "img (0.0, 2776.8801)\n", |
||
| 834 | "label (0.0, 2.0)\n", |
||
| 835 | "[[29 49 5]]\n" |
||
| 836 | ] |
||
| 837 | }, |
||
| 838 | { |
||
| 839 | "data": { |
||
| 840 | "text/plain": [ |
||
| 841 | "<matplotlib.image.AxesImage at 0x7faf5eabeb50>" |
||
| 842 | ] |
||
| 843 | }, |
||
| 844 | "execution_count": 25, |
||
| 845 | "metadata": {}, |
||
| 846 | "output_type": "execute_result" |
||
| 847 | }, |
||
| 848 | { |
||
| 849 | "data": { |
||
| 850 | "image/png": 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\n", 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| 851 | "text/plain": [ |
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| 852 | "<Figure size 1080x2160 with 2 Axes>" |
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| 853 | ] |
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| 854 | }, |
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| 855 | "metadata": { |
||
| 856 | "needs_background": "light" |
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| 857 | }, |
||
| 858 | "output_type": "display_data" |
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| 859 | } |
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| 860 | ], |
||
| 861 | "source": [ |
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| 862 | "'''\n", |
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| 863 | "Understand min and max value in image and label numpy array\n", |
||
| 864 | "'''\n", |
||
| 865 | "img = nib.load('/data/TrainingSet/images/hippocampus_003.nii.gz')\n", |
||
| 866 | "label = nib.load('/data/TrainingSet/labels/hippocampus_003.nii.gz')\n", |
||
| 867 | "img_np=img.get_fdata().astype(np.single)\n", |
||
| 868 | "label_np=label.get_fdata().astype(np.single)\n", |
||
| 869 | "print(f'img {np.amin(img_np), np.amax(img_np)}')\n", |
||
| 870 | "print(f'label {np.amin(label_np), np.amax(label_np)}')\n", |
||
| 871 | "print(np.argwhere(img_np >= np.amax(img_np)))\n", |
||
| 872 | "\n", |
||
| 873 | "plt.subplots(1,2,figsize= (15,30))\n", |
||
| 874 | "plt.subplot(1,2,1)\n", |
||
| 875 | "plt.imshow(img_np[np.argwhere(img_np >= np.amax(img_np))[0][0]-10,:,:])\n", |
||
| 876 | "plt.subplot(1,2,2)\n", |
||
| 877 | "plt.imshow(label_np[np.argwhere(img_np >= np.amax(img_np))[0][0]-10,:,:])\n" |
||
| 878 | ] |
||
| 879 | }, |
||
| 880 | { |
||
| 881 | "cell_type": "code", |
||
| 882 | "execution_count": 26, |
||
| 883 | "metadata": {}, |
||
| 884 | "outputs": [ |
||
| 885 | { |
||
| 886 | "data": { |
||
| 887 | "text/plain": [ |
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| 888 | "679.38855" |
||
| 889 | ] |
||
| 890 | }, |
||
| 891 | "execution_count": 26, |
||
| 892 | "metadata": {}, |
||
| 893 | "output_type": "execute_result" |
||
| 894 | } |
||
| 895 | ], |
||
| 896 | "source": [ |
||
| 897 | "img_np.mean()+img_np.std()" |
||
| 898 | ] |
||
| 899 | }, |
||
| 900 | { |
||
| 901 | "cell_type": "code", |
||
| 902 | "execution_count": 22, |
||
| 903 | "metadata": {}, |
||
| 904 | "outputs": [], |
||
| 905 | "source": [ |
||
| 906 | "img_np= img_np/0xff" |
||
| 907 | ] |
||
| 908 | }, |
||
| 909 | { |
||
| 910 | "cell_type": "code", |
||
| 911 | "execution_count": 28, |
||
| 912 | "metadata": {}, |
||
| 913 | "outputs": [], |
||
| 914 | "source": [ |
||
| 915 | "img_np= img_np/np.amax(img_np)" |
||
| 916 | ] |
||
| 917 | }, |
||
| 918 | { |
||
| 919 | "cell_type": "code", |
||
| 920 | "execution_count": 16, |
||
| 921 | "metadata": {}, |
||
| 922 | "outputs": [ |
||
| 923 | { |
||
| 924 | "data": { |
||
| 925 | "text/plain": [ |
||
| 926 | "1.0" |
||
| 927 | ] |
||
| 928 | }, |
||
| 929 | "execution_count": 16, |
||
| 930 | "metadata": {}, |
||
| 931 | "output_type": "execute_result" |
||
| 932 | }, |
||
| 933 | { |
||
| 934 | "data": { |
||
| 935 | "image/png": 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\n", |
||
| 936 | "text/plain": [ |
||
| 937 | "<Figure size 432x288 with 2 Axes>" |
||
| 938 | ] |
||
| 939 | }, |
||
| 940 | "metadata": { |
||
| 941 | "needs_background": "light" |
||
| 942 | }, |
||
| 943 | "output_type": "display_data" |
||
| 944 | } |
||
| 945 | ], |
||
| 946 | "source": [ |
||
| 947 | "plt.subplot(1,2,1)\n", |
||
| 948 | "plt.imshow(img_np[29-10,:,:])\n", |
||
| 949 | "plt.subplot(1,2,2)\n", |
||
| 950 | "plt.imshow(label_np[29-10,:,:])\n", |
||
| 951 | "np.amax(img_np)" |
||
| 952 | ] |
||
| 953 | }, |
||
| 954 | { |
||
| 955 | "cell_type": "code", |
||
| 956 | "execution_count": 29, |
||
| 957 | "metadata": {}, |
||
| 958 | "outputs": [ |
||
| 959 | { |
||
| 960 | "data": { |
||
| 961 | "text/plain": [ |
||
| 962 | "<function matplotlib.pyplot.show(*args, **kw)>" |
||
| 963 | ] |
||
| 964 | }, |
||
| 965 | "execution_count": 29, |
||
| 966 | "metadata": {}, |
||
| 967 | "output_type": "execute_result" |
||
| 968 | }, |
||
| 969 | { |
||
| 970 | "data": { |
||
| 971 | "image/png": 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\n", |
||
| 972 | "text/plain": [ |
||
| 973 | "<Figure size 432x288 with 1 Axes>" |
||
| 974 | ] |
||
| 975 | }, |
||
| 976 | "metadata": { |
||
| 977 | "needs_background": "light" |
||
| 978 | }, |
||
| 979 | "output_type": "display_data" |
||
| 980 | } |
||
| 981 | ], |
||
| 982 | "source": [ |
||
| 983 | "plt.hist(img_np.ravel(),bins=100)\n", |
||
| 984 | "plt.show" |
||
| 985 | ] |
||
| 986 | }, |
||
| 987 | { |
||
| 988 | "cell_type": "markdown", |
||
| 989 | "metadata": {}, |
||
| 990 | "source": [ |
||
| 991 | "## Plotting some charts" |
||
| 992 | ] |
||
| 993 | }, |
||
| 994 | { |
||
| 995 | "cell_type": "code", |
||
| 996 | "execution_count": 15, |
||
| 997 | "metadata": {}, |
||
| 998 | "outputs": [ |
||
| 999 | { |
||
| 1000 | "name": "stdout", |
||
| 1001 | "output_type": "stream", |
||
| 1002 | "text": [ |
||
| 1003 | "\u001b[0m\u001b[01;34mTestVolumes\u001b[0m/ \u001b[01;34mTrainingSet\u001b[0m/\r\n" |
||
| 1004 | ] |
||
| 1005 | } |
||
| 1006 | ], |
||
| 1007 | "source": [ |
||
| 1008 | "ls /data" |
||
| 1009 | ] |
||
| 1010 | }, |
||
| 1011 | { |
||
| 1012 | "cell_type": "code", |
||
| 1013 | "execution_count": 57, |
||
| 1014 | "metadata": {}, |
||
| 1015 | "outputs": [ |
||
| 1016 | { |
||
| 1017 | "name": "stdout", |
||
| 1018 | "output_type": "stream", |
||
| 1019 | "text": [ |
||
| 1020 | "262\n" |
||
| 1021 | ] |
||
| 1022 | }, |
||
| 1023 | { |
||
| 1024 | "data": { |
||
| 1025 | "image/png": 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\n", |
||
| 1026 | "text/plain": [ |
||
| 1027 | "<Figure size 720x720 with 1 Axes>" |
||
| 1028 | ] |
||
| 1029 | }, |
||
| 1030 | "metadata": { |
||
| 1031 | "needs_background": "light" |
||
| 1032 | }, |
||
| 1033 | "output_type": "display_data" |
||
| 1034 | } |
||
| 1035 | ], |
||
| 1036 | "source": [ |
||
| 1037 | "# TASK: Plot a histogram of all volumes that we have in our dataset and see how \n", |
||
| 1038 | "# our dataset measures against a slice of a normal population represented by the chart below.\n", |
||
| 1039 | "\n", |
||
| 1040 | "train_set_files = glob.glob('/data/TrainingSet/labels/*')\n", |
||
| 1041 | "train_set_volumes = []\n", |
||
| 1042 | "train_set = []\n", |
||
| 1043 | "\n", |
||
| 1044 | "count = 0\n", |
||
| 1045 | "\n", |
||
| 1046 | "for i in train_set_files:\n", |
||
| 1047 | " img = nib.load(i)\n", |
||
| 1048 | " img_np = img.get_fdata()\n", |
||
| 1049 | " i_vol = np.count_nonzero(img_np>0)\n", |
||
| 1050 | " i_dim = img.header['dim']\n", |
||
| 1051 | " train_set_volumes.append(i_vol)\n", |
||
| 1052 | " train_set.append([i_vol, i, i_dim])\n", |
||
| 1053 | " count+=1\n", |
||
| 1054 | "\n", |
||
| 1055 | "\n", |
||
| 1056 | "plt.hist(train_set_volumes, bins = 1000)\n", |
||
| 1057 | "plt.xlim(1000,25000)\n", |
||
| 1058 | "print(count) " |
||
| 1059 | ] |
||
| 1060 | }, |
||
| 1061 | { |
||
| 1062 | "cell_type": "code", |
||
| 1063 | "execution_count": 68, |
||
| 1064 | "metadata": {}, |
||
| 1065 | "outputs": [ |
||
| 1066 | { |
||
| 1067 | "data": { |
||
| 1068 | "text/plain": [ |
||
| 1069 | "array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)" |
||
| 1070 | ] |
||
| 1071 | }, |
||
| 1072 | "execution_count": 68, |
||
| 1073 | "metadata": {}, |
||
| 1074 | "output_type": "execute_result" |
||
| 1075 | } |
||
| 1076 | ], |
||
| 1077 | "source": [ |
||
| 1078 | "nib.load('/data/TrainingSet/images/hippocampus_083.nii.gz').header['dim']-nib.load('/data/TrainingSet/labels/hippocampus_083.nii.gz').header['dim']" |
||
| 1079 | ] |
||
| 1080 | }, |
||
| 1081 | { |
||
| 1082 | "cell_type": "code", |
||
| 1083 | "execution_count": 60, |
||
| 1084 | "metadata": {}, |
||
| 1085 | "outputs": [ |
||
| 1086 | { |
||
| 1087 | "data": { |
||
| 1088 | "text/plain": [ |
||
| 1089 | "array([[3581, '/data/TrainingSet/labels/hippocampus_376.nii.gz',\n", |
||
| 1090 | " array([ 3, 35, 55, 37, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1091 | " [3023, '/data/TrainingSet/labels/hippocampus_165.nii.gz',\n", |
||
| 1092 | " array([ 3, 34, 49, 29, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1093 | " [3172, '/data/TrainingSet/labels/hippocampus_286.nii.gz',\n", |
||
| 1094 | " array([ 3, 37, 45, 46, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1095 | " [3994, '/data/TrainingSet/labels/hippocampus_152.nii.gz',\n", |
||
| 1096 | " array([ 3, 36, 53, 37, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1097 | " [2920, '/data/TrainingSet/labels/hippocampus_176.nii.gz',\n", |
||
| 1098 | " array([ 3, 35, 50, 36, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1099 | " [3340, '/data/TrainingSet/labels/hippocampus_096.nii.gz',\n", |
||
| 1100 | " array([ 3, 34, 47, 39, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1101 | " [3000, '/data/TrainingSet/labels/hippocampus_068.nii.gz',\n", |
||
| 1102 | " array([ 3, 36, 40, 43, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1103 | " [2738, '/data/TrainingSet/labels/hippocampus_289.nii.gz',\n", |
||
| 1104 | " array([ 3, 35, 49, 36, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1105 | " [3097, '/data/TrainingSet/labels/hippocampus_260.nii.gz',\n", |
||
| 1106 | " array([ 3, 35, 53, 29, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1107 | " [3674, '/data/TrainingSet/labels/hippocampus_171.nii.gz',\n", |
||
| 1108 | " array([ 3, 35, 56, 28, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1109 | " [3918, '/data/TrainingSet/labels/hippocampus_296.nii.gz',\n", |
||
| 1110 | " array([ 3, 35, 54, 35, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1111 | " [2697, '/data/TrainingSet/labels/hippocampus_142.nii.gz',\n", |
||
| 1112 | " array([ 3, 38, 43, 41, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1113 | " [3222, '/data/TrainingSet/labels/hippocampus_375.nii.gz',\n", |
||
| 1114 | " array([ 3, 32, 54, 34, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1115 | " [2753, '/data/TrainingSet/labels/hippocampus_097.nii.gz',\n", |
||
| 1116 | " array([ 3, 37, 48, 34, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1117 | " [3660, '/data/TrainingSet/labels/hippocampus_064.nii.gz',\n", |
||
| 1118 | " array([ 3, 35, 53, 35, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1119 | " [3940, '/data/TrainingSet/labels/hippocampus_108.nii.gz',\n", |
||
| 1120 | " array([ 3, 36, 53, 37, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1121 | " [3450, '/data/TrainingSet/labels/hippocampus_070.nii.gz',\n", |
||
| 1122 | " array([ 3, 37, 50, 38, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1123 | " [3675, '/data/TrainingSet/labels/hippocampus_329.nii.gz',\n", |
||
| 1124 | " array([ 3, 34, 53, 32, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1125 | " [3946, '/data/TrainingSet/labels/hippocampus_107.nii.gz',\n", |
||
| 1126 | " array([ 3, 35, 55, 34, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1127 | " [4001, '/data/TrainingSet/labels/hippocampus_090.nii.gz',\n", |
||
| 1128 | " array([ 3, 37, 50, 40, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1129 | " [3142, '/data/TrainingSet/labels/hippocampus_092.nii.gz',\n", |
||
| 1130 | " array([ 3, 38, 49, 28, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1131 | " [3516, '/data/TrainingSet/labels/hippocampus_318.nii.gz',\n", |
||
| 1132 | " array([ 3, 37, 51, 33, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1133 | " [2635, '/data/TrainingSet/labels/hippocampus_333.nii.gz',\n", |
||
| 1134 | " array([ 3, 33, 46, 38, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1135 | " [3229, '/data/TrainingSet/labels/hippocampus_123.nii.gz',\n", |
||
| 1136 | " array([ 3, 32, 53, 38, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1137 | " [3195, '/data/TrainingSet/labels/hippocampus_037.nii.gz',\n", |
||
| 1138 | " array([ 3, 34, 51, 32, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1139 | " [3568, '/data/TrainingSet/labels/hippocampus_023.nii.gz',\n", |
||
| 1140 | " array([ 3, 35, 51, 35, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1141 | " [3997, '/data/TrainingSet/labels/hippocampus_330.nii.gz',\n", |
||
| 1142 | " array([ 3, 35, 55, 33, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1143 | " [3877, '/data/TrainingSet/labels/hippocampus_374.nii.gz',\n", |
||
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| 1583 | " [3137, '/data/TrainingSet/labels/hippocampus_124.nii.gz',\n", |
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| 1584 | " array([ 3, 35, 55, 41, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1585 | " [3054, '/data/TrainingSet/labels/hippocampus_268.nii.gz',\n", |
||
| 1586 | " array([ 3, 34, 51, 37, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1587 | " [2684, '/data/TrainingSet/labels/hippocampus_222.nii.gz',\n", |
||
| 1588 | " array([ 3, 34, 49, 36, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1589 | " [3519, '/data/TrainingSet/labels/hippocampus_053.nii.gz',\n", |
||
| 1590 | " array([ 3, 37, 51, 35, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1591 | " [3923, '/data/TrainingSet/labels/hippocampus_172.nii.gz',\n", |
||
| 1592 | " array([ 3, 34, 56, 31, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1593 | " [3600, '/data/TrainingSet/labels/hippocampus_156.nii.gz',\n", |
||
| 1594 | " array([ 3, 36, 52, 36, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1595 | " [2813, '/data/TrainingSet/labels/hippocampus_136.nii.gz',\n", |
||
| 1596 | " array([ 3, 34, 49, 41, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1597 | " [2593, '/data/TrainingSet/labels/hippocampus_177.nii.gz',\n", |
||
| 1598 | " array([ 3, 33, 44, 40, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1599 | " [3692, '/data/TrainingSet/labels/hippocampus_181.nii.gz',\n", |
||
| 1600 | " array([ 3, 33, 49, 40, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1601 | " [2912, '/data/TrainingSet/labels/hippocampus_354.nii.gz',\n", |
||
| 1602 | " array([ 3, 36, 50, 32, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1603 | " [2634, '/data/TrainingSet/labels/hippocampus_359.nii.gz',\n", |
||
| 1604 | " array([ 3, 35, 49, 35, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1605 | " [2714, '/data/TrainingSet/labels/hippocampus_141.nii.gz',\n", |
||
| 1606 | " array([ 3, 33, 44, 42, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1607 | " [3496, '/data/TrainingSet/labels/hippocampus_356.nii.gz',\n", |
||
| 1608 | " array([ 3, 36, 51, 37, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1609 | " [3834, '/data/TrainingSet/labels/hippocampus_325.nii.gz',\n", |
||
| 1610 | " array([ 3, 35, 51, 40, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1611 | " [2995, '/data/TrainingSet/labels/hippocampus_210.nii.gz',\n", |
||
| 1612 | " array([ 3, 34, 48, 40, 1, 1, 1, 1], dtype=int16)]],\n", |
||
| 1613 | " dtype=object)" |
||
| 1614 | ] |
||
| 1615 | }, |
||
| 1616 | "execution_count": 60, |
||
| 1617 | "metadata": {}, |
||
| 1618 | "output_type": "execute_result" |
||
| 1619 | } |
||
| 1620 | ], |
||
| 1621 | "source": [ |
||
| 1622 | "train_set" |
||
| 1623 | ] |
||
| 1624 | }, |
||
| 1625 | { |
||
| 1626 | "cell_type": "markdown", |
||
| 1627 | "metadata": {}, |
||
| 1628 | "source": [ |
||
| 1629 | "<img src=\"img/nomogram_fem_right.svg\" width=400 align=left>" |
||
| 1630 | ] |
||
| 1631 | }, |
||
| 1632 | { |
||
| 1633 | "cell_type": "markdown", |
||
| 1634 | "metadata": {}, |
||
| 1635 | "source": [ |
||
| 1636 | "Do you see any outliers? Why do you think it's so (might be not immediately obvious, but it's always a good idea to inspect) outliers closer. If you haven't found the images that do not belong, the histogram may help you." |
||
| 1637 | ] |
||
| 1638 | }, |
||
| 1639 | { |
||
| 1640 | "cell_type": "code", |
||
| 1641 | "execution_count": 126, |
||
| 1642 | "metadata": {}, |
||
| 1643 | "outputs": [ |
||
| 1644 | { |
||
| 1645 | "name": "stdout", |
||
| 1646 | "output_type": "stream", |
||
| 1647 | "text": [ |
||
| 1648 | "Number of hippocampus label volume greater than 4500: 1\n", |
||
| 1649 | "Number of hippocampus label volume less than 2800: 40\n", |
||
| 1650 | "Number of hippocampus label volume between 2900 and 4500 : 221\n" |
||
| 1651 | ] |
||
| 1652 | } |
||
| 1653 | ], |
||
| 1654 | "source": [ |
||
| 1655 | "train_set=np.array(train_set)\n", |
||
| 1656 | "hi_outlier = []\n", |
||
| 1657 | "lo_outlier = []\n", |
||
| 1658 | "no_outlier = []\n", |
||
| 1659 | "\n", |
||
| 1660 | "for s in train_set:\n", |
||
| 1661 | " if (int(s[0]) > 4600): \n", |
||
| 1662 | " hi_outlier.append(s)\n", |
||
| 1663 | " elif (int(s[0]) < 2800):\n", |
||
| 1664 | " lo_outlier.append(s)\n", |
||
| 1665 | " else:\n", |
||
| 1666 | " no_outlier.append(s)\n", |
||
| 1667 | "#outlier=np.array(outlier)\n", |
||
| 1668 | "\n", |
||
| 1669 | "print(f'Number of hippocampus label volume greater than 4500: {len(hi_outlier)}')\n", |
||
| 1670 | "print(f'Number of hippocampus label volume less than 2800: {len(lo_outlier)}')\n", |
||
| 1671 | "print(f'Number of hippocampus label volume between 2900 and 4500 : {len(no_outlier)}')\n" |
||
| 1672 | ] |
||
| 1673 | }, |
||
| 1674 | { |
||
| 1675 | "cell_type": "code", |
||
| 1676 | "execution_count": 71, |
||
| 1677 | "metadata": {}, |
||
| 1678 | "outputs": [ |
||
| 1679 | { |
||
| 1680 | "data": { |
||
| 1681 | "text/plain": [ |
||
| 1682 | "[array([20702, '/data/TrainingSet/labels/hippocampus_281.nii.gz',\n", |
||
| 1683 | " array([ 3, 512, 512, 94, 1, 1, 1, 1], dtype=int16)],\n", |
||
| 1684 | " dtype=object)]" |
||
| 1685 | ] |
||
| 1686 | }, |
||
| 1687 | "execution_count": 71, |
||
| 1688 | "metadata": {}, |
||
| 1689 | "output_type": "execute_result" |
||
| 1690 | } |
||
| 1691 | ], |
||
| 1692 | "source": [ |
||
| 1693 | "hi_outlier" |
||
| 1694 | ] |
||
| 1695 | }, |
||
| 1696 | { |
||
| 1697 | "cell_type": "code", |
||
| 1698 | "execution_count": 72, |
||
| 1699 | "metadata": {}, |
||
| 1700 | "outputs": [], |
||
| 1701 | "source": [ |
||
| 1702 | "hi_outlier_label = nib.load(hi_outlier[0][1])" |
||
| 1703 | ] |
||
| 1704 | }, |
||
| 1705 | { |
||
| 1706 | "cell_type": "code", |
||
| 1707 | "execution_count": 73, |
||
| 1708 | "metadata": {}, |
||
| 1709 | "outputs": [ |
||
| 1710 | { |
||
| 1711 | "name": "stdout", |
||
| 1712 | "output_type": "stream", |
||
| 1713 | "text": [ |
||
| 1714 | "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 1715 | "sizeof_hdr : 348\n", |
||
| 1716 | "data_type : b''\n", |
||
| 1717 | "db_name : b''\n", |
||
| 1718 | "extents : 0\n", |
||
| 1719 | "session_error : 0\n", |
||
| 1720 | "regular : b'r'\n", |
||
| 1721 | "dim_info : 0\n", |
||
| 1722 | "dim : [ 3 512 512 94 1 1 1 1]\n", |
||
| 1723 | "intent_p1 : 0.0\n", |
||
| 1724 | "intent_p2 : 0.0\n", |
||
| 1725 | "intent_p3 : 0.0\n", |
||
| 1726 | "intent_code : none\n", |
||
| 1727 | "datatype : uint8\n", |
||
| 1728 | "bitpix : 8\n", |
||
| 1729 | "slice_start : 0\n", |
||
| 1730 | "pixdim : [1. 0.734375 0.734375 5. 0. 0. 0. 0. ]\n", |
||
| 1731 | "vox_offset : 0.0\n", |
||
| 1732 | "scl_slope : nan\n", |
||
| 1733 | "scl_inter : nan\n", |
||
| 1734 | "slice_end : 0\n", |
||
| 1735 | "slice_code : unknown\n", |
||
| 1736 | "xyzt_units : 10\n", |
||
| 1737 | "cal_max : 0.0\n", |
||
| 1738 | "cal_min : 0.0\n", |
||
| 1739 | "slice_duration : 0.0\n", |
||
| 1740 | "toffset : 0.0\n", |
||
| 1741 | "glmax : 0\n", |
||
| 1742 | "glmin : 0\n", |
||
| 1743 | "descrip : b'5.0.10'\n", |
||
| 1744 | "aux_file : b''\n", |
||
| 1745 | "qform_code : scanner\n", |
||
| 1746 | "sform_code : scanner\n", |
||
| 1747 | "quatern_b : 0.0\n", |
||
| 1748 | "quatern_c : 0.0\n", |
||
| 1749 | "quatern_d : 0.0\n", |
||
| 1750 | "qoffset_x : -375.26562\n", |
||
| 1751 | "qoffset_y : -375.26562\n", |
||
| 1752 | "qoffset_z : 0.0\n", |
||
| 1753 | "srow_x : [ 0.734375 0. 0. -375.26562 ]\n", |
||
| 1754 | "srow_y : [ 0. 0.734375 0. -375.26562 ]\n", |
||
| 1755 | "srow_z : [0. 0. 5. 0.]\n", |
||
| 1756 | "intent_name : b''\n", |
||
| 1757 | "magic : b'n+1'\n" |
||
| 1758 | ] |
||
| 1759 | } |
||
| 1760 | ], |
||
| 1761 | "source": [ |
||
| 1762 | "print(hi_outlier_label.header)" |
||
| 1763 | ] |
||
| 1764 | }, |
||
| 1765 | { |
||
| 1766 | "cell_type": "code", |
||
| 1767 | "execution_count": 74, |
||
| 1768 | "metadata": {}, |
||
| 1769 | "outputs": [ |
||
| 1770 | { |
||
| 1771 | "name": "stdout", |
||
| 1772 | "output_type": "stream", |
||
| 1773 | "text": [ |
||
| 1774 | "(512, 512, 94)\n", |
||
| 1775 | "[1. 0.734375 0.734375 5. 0. 0. 0. 0. ]\n", |
||
| 1776 | "[ 3 512 512 94 1 1 1 1]\n", |
||
| 1777 | "[[ 0.734375 0. 0. -375.265625]\n", |
||
| 1778 | " [ 0. 0.734375 0. -375.265625]\n", |
||
| 1779 | " [ 0. 0. 5. 0. ]\n", |
||
| 1780 | " [ 0. 0. 0. 1. ]]\n" |
||
| 1781 | ] |
||
| 1782 | }, |
||
| 1783 | { |
||
| 1784 | "data": { |
||
| 1785 | "text/plain": [ |
||
| 1786 | "<matplotlib.image.AxesImage at 0x7f3a759dcee0>" |
||
| 1787 | ] |
||
| 1788 | }, |
||
| 1789 | "execution_count": 74, |
||
| 1790 | "metadata": {}, |
||
| 1791 | "output_type": "execute_result" |
||
| 1792 | }, |
||
| 1793 | { |
||
| 1794 | "data": { |
||
| 1795 | "image/png": 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\n", |
||
| 1796 | "text/plain": [ |
||
| 1797 | "<Figure size 720x720 with 1 Axes>" |
||
| 1798 | ] |
||
| 1799 | }, |
||
| 1800 | "metadata": { |
||
| 1801 | "needs_background": "light" |
||
| 1802 | }, |
||
| 1803 | "output_type": "display_data" |
||
| 1804 | } |
||
| 1805 | ], |
||
| 1806 | "source": [ |
||
| 1807 | "print(hi_outlier_label.header.get_data_shape())\n", |
||
| 1808 | "print(hi_outlier_label.header['pixdim'])\n", |
||
| 1809 | "print(hi_outlier_label.header['dim'])\n", |
||
| 1810 | "print(hi_outlier_label.header.get_sform())\n", |
||
| 1811 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_281.nii.gz').get_fdata()[:,260,:],aspect = 94/(512*2))" |
||
| 1812 | ] |
||
| 1813 | }, |
||
| 1814 | { |
||
| 1815 | "cell_type": "markdown", |
||
| 1816 | "metadata": {}, |
||
| 1817 | "source": [ |
||
| 1818 | "#### High Label Voxel volume Outlier has different dim, pixdim, and sform vectors. \n", |
||
| 1819 | "#### hippocampus_281.nii.gz is not a hippocampus region" |
||
| 1820 | ] |
||
| 1821 | }, |
||
| 1822 | { |
||
| 1823 | "cell_type": "markdown", |
||
| 1824 | "metadata": {}, |
||
| 1825 | "source": [ |
||
| 1826 | "## Labels with volume between 2900mm^3 and 4500mm^3:" |
||
| 1827 | ] |
||
| 1828 | }, |
||
| 1829 | { |
||
| 1830 | "cell_type": "code", |
||
| 1831 | "execution_count": 75, |
||
| 1832 | "metadata": {}, |
||
| 1833 | "outputs": [ |
||
| 1834 | { |
||
| 1835 | "name": "stdout", |
||
| 1836 | "output_type": "stream", |
||
| 1837 | "text": [ |
||
| 1838 | "221\n" |
||
| 1839 | ] |
||
| 1840 | } |
||
| 1841 | ], |
||
| 1842 | "source": [ |
||
| 1843 | "no_outlier_shape = {}\n", |
||
| 1844 | "no_outlier_pixdim = {}\n", |
||
| 1845 | "no_outlier_dim = {}\n", |
||
| 1846 | "no_outlier_sform = {}\n", |
||
| 1847 | "no_outlier_bitpix = {}\n", |
||
| 1848 | "count = 0\n", |
||
| 1849 | "for label in no_outlier:\n", |
||
| 1850 | " count+=1\n", |
||
| 1851 | " fp = label[1]\n", |
||
| 1852 | " keyshape = nib.load(fp).header.get_data_shape()\n", |
||
| 1853 | " no_outlier_shape.setdefault(keyshape,[])\n", |
||
| 1854 | " no_outlier_shape[keyshape].append(fp)\n", |
||
| 1855 | " \n", |
||
| 1856 | " keypixdim = str(nib.load(fp).header['pixdim'])\n", |
||
| 1857 | " no_outlier_pixdim.setdefault(keypixdim,[])\n", |
||
| 1858 | " no_outlier_pixdim[keypixdim].append(fp)\n", |
||
| 1859 | " \n", |
||
| 1860 | " keydim = tuple(nib.load(fp).header['dim'])\n", |
||
| 1861 | " no_outlier_dim.setdefault(keydim,[])\n", |
||
| 1862 | " no_outlier_dim[keydim].append(fp)\n", |
||
| 1863 | " \n", |
||
| 1864 | " keysf = str(nib.load(fp).header.get_sform())\n", |
||
| 1865 | " no_outlier_sform.setdefault(keysf,[])\n", |
||
| 1866 | " no_outlier_sform[keysf].append(fp)\n", |
||
| 1867 | " \n", |
||
| 1868 | " keybp = str(nib.load(fp).header['bitpix'])\n", |
||
| 1869 | " no_outlier_bitpix.setdefault(keybp,[])\n", |
||
| 1870 | " no_outlier_bitpix[keybp].append(fp)\n", |
||
| 1871 | "\n", |
||
| 1872 | "print(count)\n" |
||
| 1873 | ] |
||
| 1874 | }, |
||
| 1875 | { |
||
| 1876 | "cell_type": "code", |
||
| 1877 | "execution_count": 76, |
||
| 1878 | "metadata": {}, |
||
| 1879 | "outputs": [ |
||
| 1880 | { |
||
| 1881 | "data": { |
||
| 1882 | "text/plain": [ |
||
| 1883 | "dict_keys(['[1. 1. 1. 1. 1. 0. 0. 0.]'])" |
||
| 1884 | ] |
||
| 1885 | }, |
||
| 1886 | "execution_count": 76, |
||
| 1887 | "metadata": {}, |
||
| 1888 | "output_type": "execute_result" |
||
| 1889 | } |
||
| 1890 | ], |
||
| 1891 | "source": [ |
||
| 1892 | "no_outlier_pixdim.keys()" |
||
| 1893 | ] |
||
| 1894 | }, |
||
| 1895 | { |
||
| 1896 | "cell_type": "code", |
||
| 1897 | "execution_count": 77, |
||
| 1898 | "metadata": {}, |
||
| 1899 | "outputs": [ |
||
| 1900 | { |
||
| 1901 | "data": { |
||
| 1902 | "text/plain": [ |
||
| 1903 | "array([35, 34, 37, 36, 35, 34, 36, 35, 35, 35, 32, 35, 37, 34, 35, 37, 38,\n", |
||
| 1904 | " 37, 32, 34, 35, 35, 38, 35, 41, 36, 38, 36, 36, 38, 37, 31, 34, 38,\n", |
||
| 1905 | " 38, 34, 36, 35, 36, 38, 32, 38, 35, 36, 35, 33, 35, 35, 39, 36, 36,\n", |
||
| 1906 | " 37, 37, 38, 32, 38, 35, 35, 33, 34, 36, 34, 33, 35, 37, 35, 37, 35,\n", |
||
| 1907 | " 36, 36, 34, 33, 35, 33, 34, 36, 36, 34, 33, 32, 34, 36, 36, 37, 39,\n", |
||
| 1908 | " 35, 38, 33, 37, 34, 35, 34, 36, 34, 37, 33, 37, 34, 36, 37, 35, 35,\n", |
||
| 1909 | " 36, 35, 34, 38, 36, 38, 34, 37, 38, 35, 38, 37, 33, 38, 34, 39, 36,\n", |
||
| 1910 | " 40, 34, 36, 36, 34, 38, 32, 36, 36, 38, 34, 36, 36, 31, 35, 39, 35,\n", |
||
| 1911 | " 42, 34, 38, 37, 37, 36, 35, 38, 37, 35, 34, 35, 35, 38, 36, 34, 35,\n", |
||
| 1912 | " 33, 36, 37, 39, 35, 36, 33, 37, 34, 35, 35, 33, 34, 36, 37, 33, 36,\n", |
||
| 1913 | " 37, 36, 36, 37, 38, 36, 36, 35, 38, 37, 39, 33, 38, 33, 33, 37, 34,\n", |
||
| 1914 | " 41, 35, 36, 35, 34, 34, 36, 34, 33, 36, 36, 35], dtype=int16)" |
||
| 1915 | ] |
||
| 1916 | }, |
||
| 1917 | "execution_count": 77, |
||
| 1918 | "metadata": {}, |
||
| 1919 | "output_type": "execute_result" |
||
| 1920 | } |
||
| 1921 | ], |
||
| 1922 | "source": [ |
||
| 1923 | "dim_keys=[i for i in no_outlier_dim.keys()]\n", |
||
| 1924 | "dim_keys = np.array(dim_keys)\n", |
||
| 1925 | "dim_keys[:, 1]" |
||
| 1926 | ] |
||
| 1927 | }, |
||
| 1928 | { |
||
| 1929 | "cell_type": "code", |
||
| 1930 | "execution_count": 78, |
||
| 1931 | "metadata": {}, |
||
| 1932 | "outputs": [ |
||
| 1933 | { |
||
| 1934 | "data": { |
||
| 1935 | "text/plain": [ |
||
| 1936 | "(array([ 8., 17., 31., 41., 42., 26., 24., 6., 1., 3.]),\n", |
||
| 1937 | " array([31. , 32.1, 33.2, 34.3, 35.4, 36.5, 37.6, 38.7, 39.8, 40.9, 42. ]),\n", |
||
| 1938 | " <a list of 10 Patch objects>)" |
||
| 1939 | ] |
||
| 1940 | }, |
||
| 1941 | "execution_count": 78, |
||
| 1942 | "metadata": {}, |
||
| 1943 | "output_type": "execute_result" |
||
| 1944 | }, |
||
| 1945 | { |
||
| 1946 | "data": { |
||
| 1947 | "image/png": "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\n", |
||
| 1948 | "text/plain": [ |
||
| 1949 | "<Figure size 720x720 with 1 Axes>" |
||
| 1950 | ] |
||
| 1951 | }, |
||
| 1952 | "metadata": { |
||
| 1953 | "needs_background": "light" |
||
| 1954 | }, |
||
| 1955 | "output_type": "display_data" |
||
| 1956 | } |
||
| 1957 | ], |
||
| 1958 | "source": [ |
||
| 1959 | "shape_keys = [(i) for i in no_outlier_shape.keys()]\n", |
||
| 1960 | "shape_keys = np.array(shape_keys)\n", |
||
| 1961 | "plt.hist(shape_keys[:,0])" |
||
| 1962 | ] |
||
| 1963 | }, |
||
| 1964 | { |
||
| 1965 | "cell_type": "code", |
||
| 1966 | "execution_count": 79, |
||
| 1967 | "metadata": {}, |
||
| 1968 | "outputs": [ |
||
| 1969 | { |
||
| 1970 | "data": { |
||
| 1971 | "text/plain": [ |
||
| 1972 | "(array([ 2., 2., 9., 21., 46., 47., 44., 18., 6., 4.]),\n", |
||
| 1973 | " array([40. , 41.9, 43.8, 45.7, 47.6, 49.5, 51.4, 53.3, 55.2, 57.1, 59. ]),\n", |
||
| 1974 | " <a list of 10 Patch objects>)" |
||
| 1975 | ] |
||
| 1976 | }, |
||
| 1977 | "execution_count": 79, |
||
| 1978 | "metadata": {}, |
||
| 1979 | "output_type": "execute_result" |
||
| 1980 | }, |
||
| 1981 | { |
||
| 1982 | "data": { |
||
| 1983 | "image/png": "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\n", |
||
| 1984 | "text/plain": [ |
||
| 1985 | "<Figure size 720x720 with 1 Axes>" |
||
| 1986 | ] |
||
| 1987 | }, |
||
| 1988 | "metadata": { |
||
| 1989 | "needs_background": "light" |
||
| 1990 | }, |
||
| 1991 | "output_type": "display_data" |
||
| 1992 | } |
||
| 1993 | ], |
||
| 1994 | "source": [ |
||
| 1995 | "plt.hist(shape_keys[:,1])" |
||
| 1996 | ] |
||
| 1997 | }, |
||
| 1998 | { |
||
| 1999 | "cell_type": "code", |
||
| 2000 | "execution_count": 80, |
||
| 2001 | "metadata": {}, |
||
| 2002 | "outputs": [ |
||
| 2003 | { |
||
| 2004 | "data": { |
||
| 2005 | "text/plain": [ |
||
| 2006 | "array([[36, 40, 43],\n", |
||
| 2007 | " [36, 42, 41],\n", |
||
| 2008 | " [39, 41, 42]])" |
||
| 2009 | ] |
||
| 2010 | }, |
||
| 2011 | "execution_count": 80, |
||
| 2012 | "metadata": {}, |
||
| 2013 | "output_type": "execute_result" |
||
| 2014 | } |
||
| 2015 | ], |
||
| 2016 | "source": [ |
||
| 2017 | "shape_keys[shape_keys[:,1]<42.5]" |
||
| 2018 | ] |
||
| 2019 | }, |
||
| 2020 | { |
||
| 2021 | "cell_type": "code", |
||
| 2022 | "execution_count": 81, |
||
| 2023 | "metadata": {}, |
||
| 2024 | "outputs": [ |
||
| 2025 | { |
||
| 2026 | "data": { |
||
| 2027 | "text/plain": [ |
||
| 2028 | "(array([ 3., 8., 13., 36., 34., 37., 45., 13., 7., 3.]),\n", |
||
| 2029 | " array([24. , 26.3, 28.6, 30.9, 33.2, 35.5, 37.8, 40.1, 42.4, 44.7, 47. ]),\n", |
||
| 2030 | " <a list of 10 Patch objects>)" |
||
| 2031 | ] |
||
| 2032 | }, |
||
| 2033 | "execution_count": 81, |
||
| 2034 | "metadata": {}, |
||
| 2035 | "output_type": "execute_result" |
||
| 2036 | }, |
||
| 2037 | { |
||
| 2038 | "data": { |
||
| 2039 | "image/png": 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\n", |
||
| 2040 | "text/plain": [ |
||
| 2041 | "<Figure size 720x720 with 1 Axes>" |
||
| 2042 | ] |
||
| 2043 | }, |
||
| 2044 | "metadata": { |
||
| 2045 | "needs_background": "light" |
||
| 2046 | }, |
||
| 2047 | "output_type": "display_data" |
||
| 2048 | } |
||
| 2049 | ], |
||
| 2050 | "source": [ |
||
| 2051 | "plt.hist(shape_keys[:,2])" |
||
| 2052 | ] |
||
| 2053 | }, |
||
| 2054 | { |
||
| 2055 | "cell_type": "code", |
||
| 2056 | "execution_count": 82, |
||
| 2057 | "metadata": {}, |
||
| 2058 | "outputs": [ |
||
| 2059 | { |
||
| 2060 | "data": { |
||
| 2061 | "text/plain": [ |
||
| 2062 | "array([[34, 53, 24]])" |
||
| 2063 | ] |
||
| 2064 | }, |
||
| 2065 | "execution_count": 82, |
||
| 2066 | "metadata": {}, |
||
| 2067 | "output_type": "execute_result" |
||
| 2068 | } |
||
| 2069 | ], |
||
| 2070 | "source": [ |
||
| 2071 | "shape_keys[shape_keys[:,2]<25]" |
||
| 2072 | ] |
||
| 2073 | }, |
||
| 2074 | { |
||
| 2075 | "cell_type": "code", |
||
| 2076 | "execution_count": 83, |
||
| 2077 | "metadata": {}, |
||
| 2078 | "outputs": [ |
||
| 2079 | { |
||
| 2080 | "data": { |
||
| 2081 | "text/plain": [ |
||
| 2082 | "['/data/TrainingSet/labels/hippocampus_243.nii.gz']" |
||
| 2083 | ] |
||
| 2084 | }, |
||
| 2085 | "execution_count": 83, |
||
| 2086 | "metadata": {}, |
||
| 2087 | "output_type": "execute_result" |
||
| 2088 | } |
||
| 2089 | ], |
||
| 2090 | "source": [ |
||
| 2091 | "no_outlier_shape[(34,53,24)]" |
||
| 2092 | ] |
||
| 2093 | }, |
||
| 2094 | { |
||
| 2095 | "cell_type": "code", |
||
| 2096 | "execution_count": 84, |
||
| 2097 | "metadata": {}, |
||
| 2098 | "outputs": [ |
||
| 2099 | { |
||
| 2100 | "data": { |
||
| 2101 | "text/plain": [ |
||
| 2102 | "<matplotlib.image.AxesImage at 0x7f3a6ffdbb50>" |
||
| 2103 | ] |
||
| 2104 | }, |
||
| 2105 | "execution_count": 84, |
||
| 2106 | "metadata": {}, |
||
| 2107 | "output_type": "execute_result" |
||
| 2108 | }, |
||
| 2109 | { |
||
| 2110 | "data": { |
||
| 2111 | "image/png": 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\n", |
||
| 2112 | "text/plain": [ |
||
| 2113 | "<Figure size 720x720 with 1 Axes>" |
||
| 2114 | ] |
||
| 2115 | }, |
||
| 2116 | "metadata": { |
||
| 2117 | "needs_background": "light" |
||
| 2118 | }, |
||
| 2119 | "output_type": "display_data" |
||
| 2120 | } |
||
| 2121 | ], |
||
| 2122 | "source": [ |
||
| 2123 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_243.nii.gz').get_fdata()[15,:,:])" |
||
| 2124 | ] |
||
| 2125 | }, |
||
| 2126 | { |
||
| 2127 | "cell_type": "code", |
||
| 2128 | "execution_count": 85, |
||
| 2129 | "metadata": {}, |
||
| 2130 | "outputs": [ |
||
| 2131 | { |
||
| 2132 | "data": { |
||
| 2133 | "text/plain": [ |
||
| 2134 | "<matplotlib.image.AxesImage at 0x7f3a758eb490>" |
||
| 2135 | ] |
||
| 2136 | }, |
||
| 2137 | "execution_count": 85, |
||
| 2138 | "metadata": {}, |
||
| 2139 | "output_type": "execute_result" |
||
| 2140 | }, |
||
| 2141 | { |
||
| 2142 | "data": { |
||
| 2143 | "image/png": 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\n", |
||
| 2144 | "text/plain": [ |
||
| 2145 | "<Figure size 720x720 with 1 Axes>" |
||
| 2146 | ] |
||
| 2147 | }, |
||
| 2148 | "metadata": { |
||
| 2149 | "needs_background": "light" |
||
| 2150 | }, |
||
| 2151 | "output_type": "display_data" |
||
| 2152 | } |
||
| 2153 | ], |
||
| 2154 | "source": [ |
||
| 2155 | "plt.imshow(nib.load('/data/TrainingSet/labels/hippocampus_243.nii.gz').get_fdata()[15,:,:])" |
||
| 2156 | ] |
||
| 2157 | }, |
||
| 2158 | { |
||
| 2159 | "cell_type": "code", |
||
| 2160 | "execution_count": 86, |
||
| 2161 | "metadata": {}, |
||
| 2162 | "outputs": [ |
||
| 2163 | { |
||
| 2164 | "data": { |
||
| 2165 | "text/plain": [ |
||
| 2166 | "{'[[1. 0. 0. 0.]\\n [0. 1. 0. 0.]\\n [0. 0. 1. 0.]\\n [0. 0. 0. 1.]]': ['/data/TrainingSet/labels/hippocampus_376.nii.gz',\n", |
||
| 2167 | " '/data/TrainingSet/labels/hippocampus_375.nii.gz',\n", |
||
| 2168 | " '/data/TrainingSet/labels/hippocampus_040.nii.gz',\n", |
||
| 2169 | " '/data/TrainingSet/labels/hippocampus_301.nii.gz',\n", |
||
| 2170 | " '/data/TrainingSet/labels/hippocampus_361.nii.gz',\n", |
||
| 2171 | " '/data/TrainingSet/labels/hippocampus_094.nii.gz',\n", |
||
| 2172 | " '/data/TrainingSet/labels/hippocampus_245.nii.gz',\n", |
||
| 2173 | " '/data/TrainingSet/labels/hippocampus_039.nii.gz',\n", |
||
| 2174 | " '/data/TrainingSet/labels/hippocampus_302.nii.gz',\n", |
||
| 2175 | " '/data/TrainingSet/labels/hippocampus_305.nii.gz',\n", |
||
| 2176 | " '/data/TrainingSet/labels/hippocampus_093.nii.gz',\n", |
||
| 2177 | " '/data/TrainingSet/labels/hippocampus_321.nii.gz',\n", |
||
| 2178 | " '/data/TrainingSet/labels/hippocampus_331.nii.gz',\n", |
||
| 2179 | " '/data/TrainingSet/labels/hippocampus_332.nii.gz',\n", |
||
| 2180 | " '/data/TrainingSet/labels/hippocampus_322.nii.gz',\n", |
||
| 2181 | " '/data/TrainingSet/labels/hippocampus_253.nii.gz',\n", |
||
| 2182 | " '/data/TrainingSet/labels/hippocampus_074.nii.gz',\n", |
||
| 2183 | " '/data/TrainingSet/labels/hippocampus_268.nii.gz'],\n", |
||
| 2184 | " '[[1. 0. 0. 1.]\\n [0. 1. 0. 1.]\\n [0. 0. 1. 1.]\\n [0. 0. 0. 1.]]': ['/data/TrainingSet/labels/hippocampus_165.nii.gz',\n", |
||
| 2185 | " '/data/TrainingSet/labels/hippocampus_286.nii.gz',\n", |
||
| 2186 | " '/data/TrainingSet/labels/hippocampus_152.nii.gz',\n", |
||
| 2187 | " '/data/TrainingSet/labels/hippocampus_176.nii.gz',\n", |
||
| 2188 | " '/data/TrainingSet/labels/hippocampus_096.nii.gz',\n", |
||
| 2189 | " '/data/TrainingSet/labels/hippocampus_068.nii.gz',\n", |
||
| 2190 | " '/data/TrainingSet/labels/hippocampus_260.nii.gz',\n", |
||
| 2191 | " '/data/TrainingSet/labels/hippocampus_171.nii.gz',\n", |
||
| 2192 | " '/data/TrainingSet/labels/hippocampus_296.nii.gz',\n", |
||
| 2193 | " '/data/TrainingSet/labels/hippocampus_064.nii.gz',\n", |
||
| 2194 | " '/data/TrainingSet/labels/hippocampus_108.nii.gz',\n", |
||
| 2195 | " '/data/TrainingSet/labels/hippocampus_070.nii.gz',\n", |
||
| 2196 | " '/data/TrainingSet/labels/hippocampus_329.nii.gz',\n", |
||
| 2197 | " '/data/TrainingSet/labels/hippocampus_107.nii.gz',\n", |
||
| 2198 | " '/data/TrainingSet/labels/hippocampus_090.nii.gz',\n", |
||
| 2199 | " '/data/TrainingSet/labels/hippocampus_092.nii.gz',\n", |
||
| 2200 | " '/data/TrainingSet/labels/hippocampus_318.nii.gz',\n", |
||
| 2201 | " '/data/TrainingSet/labels/hippocampus_123.nii.gz',\n", |
||
| 2202 | " '/data/TrainingSet/labels/hippocampus_037.nii.gz',\n", |
||
| 2203 | " '/data/TrainingSet/labels/hippocampus_023.nii.gz',\n", |
||
| 2204 | " '/data/TrainingSet/labels/hippocampus_330.nii.gz',\n", |
||
| 2205 | " '/data/TrainingSet/labels/hippocampus_374.nii.gz',\n", |
||
| 2206 | " '/data/TrainingSet/labels/hippocampus_380.nii.gz',\n", |
||
| 2207 | " '/data/TrainingSet/labels/hippocampus_056.nii.gz',\n", |
||
| 2208 | " '/data/TrainingSet/labels/hippocampus_127.nii.gz',\n", |
||
| 2209 | " '/data/TrainingSet/labels/hippocampus_163.nii.gz',\n", |
||
| 2210 | " '/data/TrainingSet/labels/hippocampus_146.nii.gz',\n", |
||
| 2211 | " '/data/TrainingSet/labels/hippocampus_050.nii.gz',\n", |
||
| 2212 | " '/data/TrainingSet/labels/hippocampus_224.nii.gz',\n", |
||
| 2213 | " '/data/TrainingSet/labels/hippocampus_160.nii.gz',\n", |
||
| 2214 | " '/data/TrainingSet/labels/hippocampus_352.nii.gz',\n", |
||
| 2215 | " '/data/TrainingSet/labels/hippocampus_114.nii.gz',\n", |
||
| 2216 | " '/data/TrainingSet/labels/hippocampus_089.nii.gz',\n", |
||
| 2217 | " '/data/TrainingSet/labels/hippocampus_026.nii.gz',\n", |
||
| 2218 | " '/data/TrainingSet/labels/hippocampus_295.nii.gz',\n", |
||
| 2219 | " '/data/TrainingSet/labels/hippocampus_045.nii.gz',\n", |
||
| 2220 | " '/data/TrainingSet/labels/hippocampus_390.nii.gz',\n", |
||
| 2221 | " '/data/TrainingSet/labels/hippocampus_075.nii.gz',\n", |
||
| 2222 | " '/data/TrainingSet/labels/hippocampus_197.nii.gz',\n", |
||
| 2223 | " '/data/TrainingSet/labels/hippocampus_087.nii.gz',\n", |
||
| 2224 | " '/data/TrainingSet/labels/hippocampus_370.nii.gz',\n", |
||
| 2225 | " '/data/TrainingSet/labels/hippocampus_145.nii.gz',\n", |
||
| 2226 | " '/data/TrainingSet/labels/hippocampus_035.nii.gz',\n", |
||
| 2227 | " '/data/TrainingSet/labels/hippocampus_195.nii.gz',\n", |
||
| 2228 | " '/data/TrainingSet/labels/hippocampus_385.nii.gz',\n", |
||
| 2229 | " '/data/TrainingSet/labels/hippocampus_189.nii.gz',\n", |
||
| 2230 | " '/data/TrainingSet/labels/hippocampus_060.nii.gz',\n", |
||
| 2231 | " '/data/TrainingSet/labels/hippocampus_132.nii.gz',\n", |
||
| 2232 | " '/data/TrainingSet/labels/hippocampus_174.nii.gz',\n", |
||
| 2233 | " '/data/TrainingSet/labels/hippocampus_150.nii.gz',\n", |
||
| 2234 | " '/data/TrainingSet/labels/hippocampus_316.nii.gz',\n", |
||
| 2235 | " '/data/TrainingSet/labels/hippocampus_345.nii.gz',\n", |
||
| 2236 | " '/data/TrainingSet/labels/hippocampus_308.nii.gz',\n", |
||
| 2237 | " '/data/TrainingSet/labels/hippocampus_250.nii.gz',\n", |
||
| 2238 | " '/data/TrainingSet/labels/hippocampus_105.nii.gz',\n", |
||
| 2239 | " '/data/TrainingSet/labels/hippocampus_372.nii.gz',\n", |
||
| 2240 | " '/data/TrainingSet/labels/hippocampus_003.nii.gz',\n", |
||
| 2241 | " '/data/TrainingSet/labels/hippocampus_259.nii.gz',\n", |
||
| 2242 | " '/data/TrainingSet/labels/hippocampus_340.nii.gz',\n", |
||
| 2243 | " '/data/TrainingSet/labels/hippocampus_219.nii.gz',\n", |
||
| 2244 | " '/data/TrainingSet/labels/hippocampus_366.nii.gz',\n", |
||
| 2245 | " '/data/TrainingSet/labels/hippocampus_025.nii.gz',\n", |
||
| 2246 | " '/data/TrainingSet/labels/hippocampus_019.nii.gz',\n", |
||
| 2247 | " '/data/TrainingSet/labels/hippocampus_109.nii.gz',\n", |
||
| 2248 | " '/data/TrainingSet/labels/hippocampus_299.nii.gz',\n", |
||
| 2249 | " '/data/TrainingSet/labels/hippocampus_155.nii.gz',\n", |
||
| 2250 | " '/data/TrainingSet/labels/hippocampus_381.nii.gz',\n", |
||
| 2251 | " '/data/TrainingSet/labels/hippocampus_294.nii.gz',\n", |
||
| 2252 | " '/data/TrainingSet/labels/hippocampus_033.nii.gz',\n", |
||
| 2253 | " '/data/TrainingSet/labels/hippocampus_373.nii.gz',\n", |
||
| 2254 | " '/data/TrainingSet/labels/hippocampus_232.nii.gz',\n", |
||
| 2255 | " '/data/TrainingSet/labels/hippocampus_166.nii.gz',\n", |
||
| 2256 | " '/data/TrainingSet/labels/hippocampus_233.nii.gz',\n", |
||
| 2257 | " '/data/TrainingSet/labels/hippocampus_249.nii.gz',\n", |
||
| 2258 | " '/data/TrainingSet/labels/hippocampus_203.nii.gz',\n", |
||
| 2259 | " '/data/TrainingSet/labels/hippocampus_393.nii.gz',\n", |
||
| 2260 | " '/data/TrainingSet/labels/hippocampus_251.nii.gz',\n", |
||
| 2261 | " '/data/TrainingSet/labels/hippocampus_287.nii.gz',\n", |
||
| 2262 | " '/data/TrainingSet/labels/hippocampus_126.nii.gz',\n", |
||
| 2263 | " '/data/TrainingSet/labels/hippocampus_130.nii.gz',\n", |
||
| 2264 | " '/data/TrainingSet/labels/hippocampus_158.nii.gz',\n", |
||
| 2265 | " '/data/TrainingSet/labels/hippocampus_149.nii.gz',\n", |
||
| 2266 | " '/data/TrainingSet/labels/hippocampus_190.nii.gz',\n", |
||
| 2267 | " '/data/TrainingSet/labels/hippocampus_084.nii.gz',\n", |
||
| 2268 | " '/data/TrainingSet/labels/hippocampus_269.nii.gz',\n", |
||
| 2269 | " '/data/TrainingSet/labels/hippocampus_257.nii.gz',\n", |
||
| 2270 | " '/data/TrainingSet/labels/hippocampus_067.nii.gz',\n", |
||
| 2271 | " '/data/TrainingSet/labels/hippocampus_095.nii.gz',\n", |
||
| 2272 | " '/data/TrainingSet/labels/hippocampus_298.nii.gz',\n", |
||
| 2273 | " '/data/TrainingSet/labels/hippocampus_387.nii.gz',\n", |
||
| 2274 | " '/data/TrainingSet/labels/hippocampus_252.nii.gz',\n", |
||
| 2275 | " '/data/TrainingSet/labels/hippocampus_106.nii.gz',\n", |
||
| 2276 | " '/data/TrainingSet/labels/hippocampus_004.nii.gz',\n", |
||
| 2277 | " '/data/TrainingSet/labels/hippocampus_235.nii.gz',\n", |
||
| 2278 | " '/data/TrainingSet/labels/hippocampus_194.nii.gz',\n", |
||
| 2279 | " '/data/TrainingSet/labels/hippocampus_017.nii.gz',\n", |
||
| 2280 | " '/data/TrainingSet/labels/hippocampus_367.nii.gz',\n", |
||
| 2281 | " '/data/TrainingSet/labels/hippocampus_185.nii.gz',\n", |
||
| 2282 | " '/data/TrainingSet/labels/hippocampus_024.nii.gz',\n", |
||
| 2283 | " '/data/TrainingSet/labels/hippocampus_041.nii.gz',\n", |
||
| 2284 | " '/data/TrainingSet/labels/hippocampus_162.nii.gz',\n", |
||
| 2285 | " '/data/TrainingSet/labels/hippocampus_243.nii.gz',\n", |
||
| 2286 | " '/data/TrainingSet/labels/hippocampus_338.nii.gz',\n", |
||
| 2287 | " '/data/TrainingSet/labels/hippocampus_048.nii.gz',\n", |
||
| 2288 | " '/data/TrainingSet/labels/hippocampus_217.nii.gz',\n", |
||
| 2289 | " '/data/TrainingSet/labels/hippocampus_228.nii.gz',\n", |
||
| 2290 | " '/data/TrainingSet/labels/hippocampus_337.nii.gz',\n", |
||
| 2291 | " '/data/TrainingSet/labels/hippocampus_368.nii.gz',\n", |
||
| 2292 | " '/data/TrainingSet/labels/hippocampus_058.nii.gz',\n", |
||
| 2293 | " '/data/TrainingSet/labels/hippocampus_014.nii.gz',\n", |
||
| 2294 | " '/data/TrainingSet/labels/hippocampus_394.nii.gz',\n", |
||
| 2295 | " '/data/TrainingSet/labels/hippocampus_088.nii.gz',\n", |
||
| 2296 | " '/data/TrainingSet/labels/hippocampus_309.nii.gz',\n", |
||
| 2297 | " '/data/TrainingSet/labels/hippocampus_091.nii.gz',\n", |
||
| 2298 | " '/data/TrainingSet/labels/hippocampus_036.nii.gz',\n", |
||
| 2299 | " '/data/TrainingSet/labels/hippocampus_007.nii.gz',\n", |
||
| 2300 | " '/data/TrainingSet/labels/hippocampus_288.nii.gz',\n", |
||
| 2301 | " '/data/TrainingSet/labels/hippocampus_216.nii.gz',\n", |
||
| 2302 | " '/data/TrainingSet/labels/hippocampus_248.nii.gz',\n", |
||
| 2303 | " '/data/TrainingSet/labels/hippocampus_227.nii.gz',\n", |
||
| 2304 | " '/data/TrainingSet/labels/hippocampus_234.nii.gz',\n", |
||
| 2305 | " '/data/TrainingSet/labels/hippocampus_052.nii.gz',\n", |
||
| 2306 | " '/data/TrainingSet/labels/hippocampus_169.nii.gz',\n", |
||
| 2307 | " '/data/TrainingSet/labels/hippocampus_020.nii.gz',\n", |
||
| 2308 | " '/data/TrainingSet/labels/hippocampus_265.nii.gz',\n", |
||
| 2309 | " '/data/TrainingSet/labels/hippocampus_212.nii.gz',\n", |
||
| 2310 | " '/data/TrainingSet/labels/hippocampus_133.nii.gz',\n", |
||
| 2311 | " '/data/TrainingSet/labels/hippocampus_006.nii.gz',\n", |
||
| 2312 | " '/data/TrainingSet/labels/hippocampus_015.nii.gz',\n", |
||
| 2313 | " '/data/TrainingSet/labels/hippocampus_360.nii.gz',\n", |
||
| 2314 | " '/data/TrainingSet/labels/hippocampus_101.nii.gz',\n", |
||
| 2315 | " '/data/TrainingSet/labels/hippocampus_311.nii.gz',\n", |
||
| 2316 | " '/data/TrainingSet/labels/hippocampus_238.nii.gz',\n", |
||
| 2317 | " '/data/TrainingSet/labels/hippocampus_001.nii.gz',\n", |
||
| 2318 | " '/data/TrainingSet/labels/hippocampus_261.nii.gz',\n", |
||
| 2319 | " '/data/TrainingSet/labels/hippocampus_207.nii.gz',\n", |
||
| 2320 | " '/data/TrainingSet/labels/hippocampus_184.nii.gz',\n", |
||
| 2321 | " '/data/TrainingSet/labels/hippocampus_044.nii.gz',\n", |
||
| 2322 | " '/data/TrainingSet/labels/hippocampus_386.nii.gz',\n", |
||
| 2323 | " '/data/TrainingSet/labels/hippocampus_290.nii.gz',\n", |
||
| 2324 | " '/data/TrainingSet/labels/hippocampus_310.nii.gz',\n", |
||
| 2325 | " '/data/TrainingSet/labels/hippocampus_148.nii.gz',\n", |
||
| 2326 | " '/data/TrainingSet/labels/hippocampus_264.nii.gz',\n", |
||
| 2327 | " '/data/TrainingSet/labels/hippocampus_358.nii.gz',\n", |
||
| 2328 | " '/data/TrainingSet/labels/hippocampus_276.nii.gz',\n", |
||
| 2329 | " '/data/TrainingSet/labels/hippocampus_363.nii.gz',\n", |
||
| 2330 | " '/data/TrainingSet/labels/hippocampus_204.nii.gz',\n", |
||
| 2331 | " '/data/TrainingSet/labels/hippocampus_389.nii.gz',\n", |
||
| 2332 | " '/data/TrainingSet/labels/hippocampus_230.nii.gz',\n", |
||
| 2333 | " '/data/TrainingSet/labels/hippocampus_350.nii.gz',\n", |
||
| 2334 | " '/data/TrainingSet/labels/hippocampus_051.nii.gz',\n", |
||
| 2335 | " '/data/TrainingSet/labels/hippocampus_304.nii.gz',\n", |
||
| 2336 | " '/data/TrainingSet/labels/hippocampus_188.nii.gz',\n", |
||
| 2337 | " '/data/TrainingSet/labels/hippocampus_065.nii.gz',\n", |
||
| 2338 | " '/data/TrainingSet/labels/hippocampus_104.nii.gz',\n", |
||
| 2339 | " '/data/TrainingSet/labels/hippocampus_351.nii.gz',\n", |
||
| 2340 | " '/data/TrainingSet/labels/hippocampus_008.nii.gz',\n", |
||
| 2341 | " '/data/TrainingSet/labels/hippocampus_317.nii.gz',\n", |
||
| 2342 | " '/data/TrainingSet/labels/hippocampus_236.nii.gz',\n", |
||
| 2343 | " '/data/TrainingSet/labels/hippocampus_170.nii.gz',\n", |
||
| 2344 | " '/data/TrainingSet/labels/hippocampus_077.nii.gz',\n", |
||
| 2345 | " '/data/TrainingSet/labels/hippocampus_173.nii.gz',\n", |
||
| 2346 | " '/data/TrainingSet/labels/hippocampus_049.nii.gz',\n", |
||
| 2347 | " '/data/TrainingSet/labels/hippocampus_292.nii.gz',\n", |
||
| 2348 | " '/data/TrainingSet/labels/hippocampus_229.nii.gz',\n", |
||
| 2349 | " '/data/TrainingSet/labels/hippocampus_263.nii.gz',\n", |
||
| 2350 | " '/data/TrainingSet/labels/hippocampus_314.nii.gz',\n", |
||
| 2351 | " '/data/TrainingSet/labels/hippocampus_355.nii.gz',\n", |
||
| 2352 | " '/data/TrainingSet/labels/hippocampus_011.nii.gz',\n", |
||
| 2353 | " '/data/TrainingSet/labels/hippocampus_098.nii.gz',\n", |
||
| 2354 | " '/data/TrainingSet/labels/hippocampus_161.nii.gz',\n", |
||
| 2355 | " '/data/TrainingSet/labels/hippocampus_102.nii.gz',\n", |
||
| 2356 | " '/data/TrainingSet/labels/hippocampus_154.nii.gz',\n", |
||
| 2357 | " '/data/TrainingSet/labels/hippocampus_157.nii.gz',\n", |
||
| 2358 | " '/data/TrainingSet/labels/hippocampus_327.nii.gz',\n", |
||
| 2359 | " '/data/TrainingSet/labels/hippocampus_215.nii.gz',\n", |
||
| 2360 | " '/data/TrainingSet/labels/hippocampus_242.nii.gz',\n", |
||
| 2361 | " '/data/TrainingSet/labels/hippocampus_046.nii.gz',\n", |
||
| 2362 | " '/data/TrainingSet/labels/hippocampus_034.nii.gz',\n", |
||
| 2363 | " '/data/TrainingSet/labels/hippocampus_223.nii.gz',\n", |
||
| 2364 | " '/data/TrainingSet/labels/hippocampus_328.nii.gz',\n", |
||
| 2365 | " '/data/TrainingSet/labels/hippocampus_038.nii.gz',\n", |
||
| 2366 | " '/data/TrainingSet/labels/hippocampus_220.nii.gz',\n", |
||
| 2367 | " '/data/TrainingSet/labels/hippocampus_383.nii.gz',\n", |
||
| 2368 | " '/data/TrainingSet/labels/hippocampus_244.nii.gz',\n", |
||
| 2369 | " '/data/TrainingSet/labels/hippocampus_277.nii.gz',\n", |
||
| 2370 | " '/data/TrainingSet/labels/hippocampus_083.nii.gz',\n", |
||
| 2371 | " '/data/TrainingSet/labels/hippocampus_010.nii.gz',\n", |
||
| 2372 | " '/data/TrainingSet/labels/hippocampus_042.nii.gz',\n", |
||
| 2373 | " '/data/TrainingSet/labels/hippocampus_300.nii.gz',\n", |
||
| 2374 | " '/data/TrainingSet/labels/hippocampus_164.nii.gz',\n", |
||
| 2375 | " '/data/TrainingSet/labels/hippocampus_303.nii.gz',\n", |
||
| 2376 | " '/data/TrainingSet/labels/hippocampus_326.nii.gz',\n", |
||
| 2377 | " '/data/TrainingSet/labels/hippocampus_124.nii.gz',\n", |
||
| 2378 | " '/data/TrainingSet/labels/hippocampus_053.nii.gz',\n", |
||
| 2379 | " '/data/TrainingSet/labels/hippocampus_172.nii.gz',\n", |
||
| 2380 | " '/data/TrainingSet/labels/hippocampus_156.nii.gz',\n", |
||
| 2381 | " '/data/TrainingSet/labels/hippocampus_136.nii.gz',\n", |
||
| 2382 | " '/data/TrainingSet/labels/hippocampus_181.nii.gz',\n", |
||
| 2383 | " '/data/TrainingSet/labels/hippocampus_354.nii.gz',\n", |
||
| 2384 | " '/data/TrainingSet/labels/hippocampus_356.nii.gz',\n", |
||
| 2385 | " '/data/TrainingSet/labels/hippocampus_325.nii.gz',\n", |
||
| 2386 | " '/data/TrainingSet/labels/hippocampus_210.nii.gz']}" |
||
| 2387 | ] |
||
| 2388 | }, |
||
| 2389 | "execution_count": 86, |
||
| 2390 | "metadata": {}, |
||
| 2391 | "output_type": "execute_result" |
||
| 2392 | } |
||
| 2393 | ], |
||
| 2394 | "source": [ |
||
| 2395 | "no_outlier_sform" |
||
| 2396 | ] |
||
| 2397 | }, |
||
| 2398 | { |
||
| 2399 | "cell_type": "code", |
||
| 2400 | "execution_count": 87, |
||
| 2401 | "metadata": {}, |
||
| 2402 | "outputs": [ |
||
| 2403 | { |
||
| 2404 | "data": { |
||
| 2405 | "text/plain": [ |
||
| 2406 | "dict_keys(['8', '32'])" |
||
| 2407 | ] |
||
| 2408 | }, |
||
| 2409 | "execution_count": 87, |
||
| 2410 | "metadata": {}, |
||
| 2411 | "output_type": "execute_result" |
||
| 2412 | } |
||
| 2413 | ], |
||
| 2414 | "source": [ |
||
| 2415 | "no_outlier_bitpix.keys()" |
||
| 2416 | ] |
||
| 2417 | }, |
||
| 2418 | { |
||
| 2419 | "cell_type": "code", |
||
| 2420 | "execution_count": 88, |
||
| 2421 | "metadata": {}, |
||
| 2422 | "outputs": [ |
||
| 2423 | { |
||
| 2424 | "data": { |
||
| 2425 | "text/plain": [ |
||
| 2426 | "['/data/TrainingSet/labels/hippocampus_003.nii.gz',\n", |
||
| 2427 | " '/data/TrainingSet/labels/hippocampus_243.nii.gz']" |
||
| 2428 | ] |
||
| 2429 | }, |
||
| 2430 | "execution_count": 88, |
||
| 2431 | "metadata": {}, |
||
| 2432 | "output_type": "execute_result" |
||
| 2433 | } |
||
| 2434 | ], |
||
| 2435 | "source": [ |
||
| 2436 | "no_outlier_bitpix['32']" |
||
| 2437 | ] |
||
| 2438 | }, |
||
| 2439 | { |
||
| 2440 | "cell_type": "code", |
||
| 2441 | "execution_count": 89, |
||
| 2442 | "metadata": {}, |
||
| 2443 | "outputs": [ |
||
| 2444 | { |
||
| 2445 | "name": "stdout", |
||
| 2446 | "output_type": "stream", |
||
| 2447 | "text": [ |
||
| 2448 | "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 2449 | "sizeof_hdr : 348\n", |
||
| 2450 | "data_type : b''\n", |
||
| 2451 | "db_name : b''\n", |
||
| 2452 | "extents : 0\n", |
||
| 2453 | "session_error : 0\n", |
||
| 2454 | "regular : b'r'\n", |
||
| 2455 | "dim_info : 0\n", |
||
| 2456 | "dim : [ 3 34 52 35 1 1 1 1]\n", |
||
| 2457 | "intent_p1 : 0.0\n", |
||
| 2458 | "intent_p2 : 0.0\n", |
||
| 2459 | "intent_p3 : 0.0\n", |
||
| 2460 | "intent_code : none\n", |
||
| 2461 | "datatype : float32\n", |
||
| 2462 | "bitpix : 32\n", |
||
| 2463 | "slice_start : 0\n", |
||
| 2464 | "pixdim : [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 2465 | "vox_offset : 0.0\n", |
||
| 2466 | "scl_slope : nan\n", |
||
| 2467 | "scl_inter : nan\n", |
||
| 2468 | "slice_end : 0\n", |
||
| 2469 | "slice_code : unknown\n", |
||
| 2470 | "xyzt_units : 10\n", |
||
| 2471 | "cal_max : 0.0\n", |
||
| 2472 | "cal_min : 0.0\n", |
||
| 2473 | "slice_duration : 0.0\n", |
||
| 2474 | "toffset : 0.0\n", |
||
| 2475 | "glmax : 0\n", |
||
| 2476 | "glmin : 0\n", |
||
| 2477 | "descrip : b'5.0.10'\n", |
||
| 2478 | "aux_file : b'none'\n", |
||
| 2479 | "qform_code : scanner\n", |
||
| 2480 | "sform_code : scanner\n", |
||
| 2481 | "quatern_b : 0.0\n", |
||
| 2482 | "quatern_c : 0.0\n", |
||
| 2483 | "quatern_d : 0.0\n", |
||
| 2484 | "qoffset_x : 1.0\n", |
||
| 2485 | "qoffset_y : 1.0\n", |
||
| 2486 | "qoffset_z : 1.0\n", |
||
| 2487 | "srow_x : [1. 0. 0. 1.]\n", |
||
| 2488 | "srow_y : [0. 1. 0. 1.]\n", |
||
| 2489 | "srow_z : [0. 0. 1. 1.]\n", |
||
| 2490 | "intent_name : b''\n", |
||
| 2491 | "magic : b'n+1'\n" |
||
| 2492 | ] |
||
| 2493 | } |
||
| 2494 | ], |
||
| 2495 | "source": [ |
||
| 2496 | "print(nib.load(no_outlier_bitpix['32'][0]).header)" |
||
| 2497 | ] |
||
| 2498 | }, |
||
| 2499 | { |
||
| 2500 | "cell_type": "code", |
||
| 2501 | "execution_count": 90, |
||
| 2502 | "metadata": {}, |
||
| 2503 | "outputs": [ |
||
| 2504 | { |
||
| 2505 | "data": { |
||
| 2506 | "text/plain": [ |
||
| 2507 | "<matplotlib.image.AxesImage at 0x7f3a7586efa0>" |
||
| 2508 | ] |
||
| 2509 | }, |
||
| 2510 | "execution_count": 90, |
||
| 2511 | "metadata": {}, |
||
| 2512 | "output_type": "execute_result" |
||
| 2513 | }, |
||
| 2514 | { |
||
| 2515 | "data": { |
||
| 2516 | "image/png": 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\n", |
||
| 2517 | "text/plain": [ |
||
| 2518 | "<Figure size 720x720 with 1 Axes>" |
||
| 2519 | ] |
||
| 2520 | }, |
||
| 2521 | "metadata": { |
||
| 2522 | "needs_background": "light" |
||
| 2523 | }, |
||
| 2524 | "output_type": "display_data" |
||
| 2525 | } |
||
| 2526 | ], |
||
| 2527 | "source": [ |
||
| 2528 | "plt.imshow(nib.load(no_outlier_bitpix['32'][0]).get_fdata()[14,:,:])" |
||
| 2529 | ] |
||
| 2530 | }, |
||
| 2531 | { |
||
| 2532 | "cell_type": "code", |
||
| 2533 | "execution_count": 91, |
||
| 2534 | "metadata": {}, |
||
| 2535 | "outputs": [ |
||
| 2536 | { |
||
| 2537 | "data": { |
||
| 2538 | "text/plain": [ |
||
| 2539 | "<matplotlib.image.AxesImage at 0x7f3a756fb0d0>" |
||
| 2540 | ] |
||
| 2541 | }, |
||
| 2542 | "execution_count": 91, |
||
| 2543 | "metadata": {}, |
||
| 2544 | "output_type": "execute_result" |
||
| 2545 | }, |
||
| 2546 | { |
||
| 2547 | "data": { |
||
| 2548 | "image/png": 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\n", |
||
| 2549 | "text/plain": [ |
||
| 2550 | "<Figure size 720x720 with 1 Axes>" |
||
| 2551 | ] |
||
| 2552 | }, |
||
| 2553 | "metadata": { |
||
| 2554 | "needs_background": "light" |
||
| 2555 | }, |
||
| 2556 | "output_type": "display_data" |
||
| 2557 | } |
||
| 2558 | ], |
||
| 2559 | "source": [ |
||
| 2560 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_003.nii.gz').get_fdata()[14,:,:])" |
||
| 2561 | ] |
||
| 2562 | }, |
||
| 2563 | { |
||
| 2564 | "cell_type": "code", |
||
| 2565 | "execution_count": 92, |
||
| 2566 | "metadata": {}, |
||
| 2567 | "outputs": [ |
||
| 2568 | { |
||
| 2569 | "data": { |
||
| 2570 | "text/plain": [ |
||
| 2571 | "<matplotlib.image.AxesImage at 0x7f3a6fc483a0>" |
||
| 2572 | ] |
||
| 2573 | }, |
||
| 2574 | "execution_count": 92, |
||
| 2575 | "metadata": {}, |
||
| 2576 | "output_type": "execute_result" |
||
| 2577 | }, |
||
| 2578 | { |
||
| 2579 | "data": { |
||
| 2580 | "image/png": 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\n", |
||
| 2581 | "text/plain": [ |
||
| 2582 | "<Figure size 720x720 with 1 Axes>" |
||
| 2583 | ] |
||
| 2584 | }, |
||
| 2585 | "metadata": { |
||
| 2586 | "needs_background": "light" |
||
| 2587 | }, |
||
| 2588 | "output_type": "display_data" |
||
| 2589 | } |
||
| 2590 | ], |
||
| 2591 | "source": [ |
||
| 2592 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_003.nii.gz').get_fdata()[:,:,14])" |
||
| 2593 | ] |
||
| 2594 | }, |
||
| 2595 | { |
||
| 2596 | "cell_type": "code", |
||
| 2597 | "execution_count": 93, |
||
| 2598 | "metadata": {}, |
||
| 2599 | "outputs": [ |
||
| 2600 | { |
||
| 2601 | "name": "stdout", |
||
| 2602 | "output_type": "stream", |
||
| 2603 | "text": [ |
||
| 2604 | "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n", |
||
| 2605 | "sizeof_hdr : 348\n", |
||
| 2606 | "data_type : b''\n", |
||
| 2607 | "db_name : b''\n", |
||
| 2608 | "extents : 0\n", |
||
| 2609 | "session_error : 0\n", |
||
| 2610 | "regular : b'r'\n", |
||
| 2611 | "dim_info : 0\n", |
||
| 2612 | "dim : [ 3 34 53 24 1 1 1 1]\n", |
||
| 2613 | "intent_p1 : 0.0\n", |
||
| 2614 | "intent_p2 : 0.0\n", |
||
| 2615 | "intent_p3 : 0.0\n", |
||
| 2616 | "intent_code : none\n", |
||
| 2617 | "datatype : float32\n", |
||
| 2618 | "bitpix : 32\n", |
||
| 2619 | "slice_start : 0\n", |
||
| 2620 | "pixdim : [1. 1. 1. 1. 1. 0. 0. 0.]\n", |
||
| 2621 | "vox_offset : 0.0\n", |
||
| 2622 | "scl_slope : nan\n", |
||
| 2623 | "scl_inter : nan\n", |
||
| 2624 | "slice_end : 0\n", |
||
| 2625 | "slice_code : unknown\n", |
||
| 2626 | "xyzt_units : 10\n", |
||
| 2627 | "cal_max : 0.0\n", |
||
| 2628 | "cal_min : 0.0\n", |
||
| 2629 | "slice_duration : 0.0\n", |
||
| 2630 | "toffset : 0.0\n", |
||
| 2631 | "glmax : 0\n", |
||
| 2632 | "glmin : 0\n", |
||
| 2633 | "descrip : b'5.0.10'\n", |
||
| 2634 | "aux_file : b'none'\n", |
||
| 2635 | "qform_code : scanner\n", |
||
| 2636 | "sform_code : scanner\n", |
||
| 2637 | "quatern_b : 0.0\n", |
||
| 2638 | "quatern_c : 0.0\n", |
||
| 2639 | "quatern_d : 0.0\n", |
||
| 2640 | "qoffset_x : 1.0\n", |
||
| 2641 | "qoffset_y : 1.0\n", |
||
| 2642 | "qoffset_z : 1.0\n", |
||
| 2643 | "srow_x : [1. 0. 0. 1.]\n", |
||
| 2644 | "srow_y : [0. 1. 0. 1.]\n", |
||
| 2645 | "srow_z : [0. 0. 1. 1.]\n", |
||
| 2646 | "intent_name : b''\n", |
||
| 2647 | "magic : b'n+1'\n" |
||
| 2648 | ] |
||
| 2649 | } |
||
| 2650 | ], |
||
| 2651 | "source": [ |
||
| 2652 | "print(nib.load(no_outlier_bitpix['32'][1]).header)" |
||
| 2653 | ] |
||
| 2654 | }, |
||
| 2655 | { |
||
| 2656 | "cell_type": "code", |
||
| 2657 | "execution_count": 94, |
||
| 2658 | "metadata": {}, |
||
| 2659 | "outputs": [ |
||
| 2660 | { |
||
| 2661 | "data": { |
||
| 2662 | "text/plain": [ |
||
| 2663 | "<matplotlib.image.AxesImage at 0x7f3a756b4730>" |
||
| 2664 | ] |
||
| 2665 | }, |
||
| 2666 | "execution_count": 94, |
||
| 2667 | "metadata": {}, |
||
| 2668 | "output_type": "execute_result" |
||
| 2669 | }, |
||
| 2670 | { |
||
| 2671 | "data": { |
||
| 2672 | "image/png": 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\n", |
||
| 2673 | "text/plain": [ |
||
| 2674 | "<Figure size 720x720 with 1 Axes>" |
||
| 2675 | ] |
||
| 2676 | }, |
||
| 2677 | "metadata": { |
||
| 2678 | "needs_background": "light" |
||
| 2679 | }, |
||
| 2680 | "output_type": "display_data" |
||
| 2681 | } |
||
| 2682 | ], |
||
| 2683 | "source": [ |
||
| 2684 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_243.nii.gz').get_fdata()[16,:,:])" |
||
| 2685 | ] |
||
| 2686 | }, |
||
| 2687 | { |
||
| 2688 | "cell_type": "markdown", |
||
| 2689 | "metadata": {}, |
||
| 2690 | "source": [ |
||
| 2691 | "#### Two NIFTI files have bitpix of 32, while the rest of label data set within acceptable hippocampus volume range have bitpix of 8. May need to rescale these two files or remove from dataset for training." |
||
| 2692 | ] |
||
| 2693 | }, |
||
| 2694 | { |
||
| 2695 | "cell_type": "markdown", |
||
| 2696 | "metadata": {}, |
||
| 2697 | "source": [ |
||
| 2698 | "## Labels with hippocampus volume less than 2850mm^3, which is below the 2.5th Percentile for any age in the range 52-71" |
||
| 2699 | ] |
||
| 2700 | }, |
||
| 2701 | { |
||
| 2702 | "cell_type": "code", |
||
| 2703 | "execution_count": 96, |
||
| 2704 | "metadata": {}, |
||
| 2705 | "outputs": [ |
||
| 2706 | { |
||
| 2707 | "data": { |
||
| 2708 | "text/plain": [ |
||
| 2709 | "[array([2738, '/data/TrainingSet/labels/hippocampus_289.nii.gz',\n", |
||
| 2710 | " array([ 3, 35, 49, 36, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2711 | " array([2697, '/data/TrainingSet/labels/hippocampus_142.nii.gz',\n", |
||
| 2712 | " array([ 3, 38, 43, 41, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2713 | " array([2753, '/data/TrainingSet/labels/hippocampus_097.nii.gz',\n", |
||
| 2714 | " array([ 3, 37, 48, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2715 | " array([2635, '/data/TrainingSet/labels/hippocampus_333.nii.gz',\n", |
||
| 2716 | " array([ 3, 33, 46, 38, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2717 | " array([2382, '/data/TrainingSet/labels/hippocampus_279.nii.gz',\n", |
||
| 2718 | " array([ 3, 34, 50, 32, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2719 | " array([2704, '/data/TrainingSet/labels/hippocampus_205.nii.gz',\n", |
||
| 2720 | " array([ 3, 32, 47, 32, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2721 | " array([2475, '/data/TrainingSet/labels/hippocampus_225.nii.gz',\n", |
||
| 2722 | " array([ 3, 33, 53, 26, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2723 | " array([2593, '/data/TrainingSet/labels/hippocampus_336.nii.gz',\n", |
||
| 2724 | " array([ 3, 34, 47, 43, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2725 | " array([2773, '/data/TrainingSet/labels/hippocampus_057.nii.gz',\n", |
||
| 2726 | " array([ 3, 35, 51, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2727 | " array([2588, '/data/TrainingSet/labels/hippocampus_341.nii.gz',\n", |
||
| 2728 | " array([ 3, 34, 48, 35, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2729 | " array([2757, '/data/TrainingSet/labels/hippocampus_378.nii.gz',\n", |
||
| 2730 | " array([ 3, 35, 52, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2731 | " array([2534, '/data/TrainingSet/labels/hippocampus_138.nii.gz',\n", |
||
| 2732 | " array([ 3, 32, 46, 42, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2733 | " array([2678, '/data/TrainingSet/labels/hippocampus_334.nii.gz',\n", |
||
| 2734 | " array([ 3, 34, 47, 36, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2735 | " array([2416, '/data/TrainingSet/labels/hippocampus_282.nii.gz',\n", |
||
| 2736 | " array([ 3, 37, 52, 32, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2737 | " array([2546, '/data/TrainingSet/labels/hippocampus_226.nii.gz',\n", |
||
| 2738 | " array([ 3, 32, 51, 28, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2739 | " array([2535, '/data/TrainingSet/labels/hippocampus_099.nii.gz',\n", |
||
| 2740 | " array([ 3, 33, 52, 27, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2741 | " array([2570, '/data/TrainingSet/labels/hippocampus_199.nii.gz',\n", |
||
| 2742 | " array([ 3, 37, 52, 26, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2743 | " array([2613, '/data/TrainingSet/labels/hippocampus_280.nii.gz',\n", |
||
| 2744 | " array([ 3, 37, 47, 32, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2745 | " array([2629, '/data/TrainingSet/labels/hippocampus_135.nii.gz',\n", |
||
| 2746 | " array([ 3, 32, 49, 38, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2747 | " array([2739, '/data/TrainingSet/labels/hippocampus_175.nii.gz',\n", |
||
| 2748 | " array([ 3, 33, 47, 35, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2749 | " array([2471, '/data/TrainingSet/labels/hippocampus_144.nii.gz',\n", |
||
| 2750 | " array([ 3, 34, 45, 43, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2751 | " array([2647, '/data/TrainingSet/labels/hippocampus_231.nii.gz',\n", |
||
| 2752 | " array([ 3, 33, 47, 42, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2753 | " array([2714, '/data/TrainingSet/labels/hippocampus_178.nii.gz',\n", |
||
| 2754 | " array([ 3, 35, 44, 41, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2755 | " array([2708, '/data/TrainingSet/labels/hippocampus_193.nii.gz',\n", |
||
| 2756 | " array([ 3, 33, 50, 29, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2757 | " array([2618, '/data/TrainingSet/labels/hippocampus_343.nii.gz',\n", |
||
| 2758 | " array([ 3, 32, 45, 38, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2759 | " array([2726, '/data/TrainingSet/labels/hippocampus_125.nii.gz',\n", |
||
| 2760 | " array([ 3, 43, 42, 39, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2761 | " array([2397, '/data/TrainingSet/labels/hippocampus_143.nii.gz',\n", |
||
| 2762 | " array([ 3, 32, 45, 41, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2763 | " array([2755, '/data/TrainingSet/labels/hippocampus_353.nii.gz',\n", |
||
| 2764 | " array([ 3, 32, 51, 31, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2765 | " array([2532, '/data/TrainingSet/labels/hippocampus_335.nii.gz',\n", |
||
| 2766 | " array([ 3, 32, 47, 41, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2767 | " array([2448, '/data/TrainingSet/labels/hippocampus_221.nii.gz',\n", |
||
| 2768 | " array([ 3, 32, 48, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2769 | " array([2451, '/data/TrainingSet/labels/hippocampus_320.nii.gz',\n", |
||
| 2770 | " array([ 3, 33, 47, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2771 | " array([2665, '/data/TrainingSet/labels/hippocampus_274.nii.gz',\n", |
||
| 2772 | " array([ 3, 35, 40, 40, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2773 | " array([2786, '/data/TrainingSet/labels/hippocampus_297.nii.gz',\n", |
||
| 2774 | " array([ 3, 34, 51, 30, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2775 | " array([2422, '/data/TrainingSet/labels/hippocampus_319.nii.gz',\n", |
||
| 2776 | " array([ 3, 33, 48, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2777 | " array([2678, '/data/TrainingSet/labels/hippocampus_180.nii.gz',\n", |
||
| 2778 | " array([ 3, 37, 45, 36, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2779 | " array([2590, '/data/TrainingSet/labels/hippocampus_349.nii.gz',\n", |
||
| 2780 | " array([ 3, 34, 50, 34, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2781 | " array([2684, '/data/TrainingSet/labels/hippocampus_222.nii.gz',\n", |
||
| 2782 | " array([ 3, 34, 49, 36, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2783 | " array([2593, '/data/TrainingSet/labels/hippocampus_177.nii.gz',\n", |
||
| 2784 | " array([ 3, 33, 44, 40, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2785 | " array([2634, '/data/TrainingSet/labels/hippocampus_359.nii.gz',\n", |
||
| 2786 | " array([ 3, 35, 49, 35, 1, 1, 1, 1], dtype=int16)], dtype=object),\n", |
||
| 2787 | " array([2714, '/data/TrainingSet/labels/hippocampus_141.nii.gz',\n", |
||
| 2788 | " array([ 3, 33, 44, 42, 1, 1, 1, 1], dtype=int16)], dtype=object)]" |
||
| 2789 | ] |
||
| 2790 | }, |
||
| 2791 | "execution_count": 96, |
||
| 2792 | "metadata": {}, |
||
| 2793 | "output_type": "execute_result" |
||
| 2794 | } |
||
| 2795 | ], |
||
| 2796 | "source": [ |
||
| 2797 | "lo_outlier" |
||
| 2798 | ] |
||
| 2799 | }, |
||
| 2800 | { |
||
| 2801 | "cell_type": "code", |
||
| 2802 | "execution_count": 100, |
||
| 2803 | "metadata": {}, |
||
| 2804 | "outputs": [], |
||
| 2805 | "source": [ |
||
| 2806 | "lo_outlier_shape = {}\n", |
||
| 2807 | "lo_outlier_pixdim = {}\n", |
||
| 2808 | "lo_outlier_sform = {}\n", |
||
| 2809 | "lo_outlier_bitpix = {}\n", |
||
| 2810 | "\n", |
||
| 2811 | "for label in lo_outlier:\n", |
||
| 2812 | " fp = label[1]\n", |
||
| 2813 | " keyshape = nib.load(fp).header.get_data_shape()\n", |
||
| 2814 | " lo_outlier_shape.setdefault(keyshape,[])\n", |
||
| 2815 | " lo_outlier_shape[keyshape].append(fp)\n", |
||
| 2816 | " \n", |
||
| 2817 | " keypixdim = str(nib.load(fp).header['pixdim'])\n", |
||
| 2818 | " lo_outlier_pixdim.setdefault(keypixdim,[])\n", |
||
| 2819 | " lo_outlier_pixdim[keypixdim].append(fp)\n", |
||
| 2820 | " \n", |
||
| 2821 | " keysf = str(nib.load(fp).header.get_sform())\n", |
||
| 2822 | " lo_outlier_sform.setdefault(keysf,[])\n", |
||
| 2823 | " lo_outlier_sform[keysf].append(fp)\n", |
||
| 2824 | " \n", |
||
| 2825 | " keybp = str(nib.load(fp).header['bitpix'])\n", |
||
| 2826 | " lo_outlier_bitpix.setdefault(keybp,[])\n", |
||
| 2827 | " lo_outlier_bitpix[keybp].append(fp)" |
||
| 2828 | ] |
||
| 2829 | }, |
||
| 2830 | { |
||
| 2831 | "cell_type": "code", |
||
| 2832 | "execution_count": 101, |
||
| 2833 | "metadata": {}, |
||
| 2834 | "outputs": [ |
||
| 2835 | { |
||
| 2836 | "data": { |
||
| 2837 | "text/plain": [ |
||
| 2838 | "(array([1., 1., 1., 7., 2., 8., 8., 3., 4., 5.]),\n", |
||
| 2839 | " array([40. , 41.3, 42.6, 43.9, 45.2, 46.5, 47.8, 49.1, 50.4, 51.7, 53. ]),\n", |
||
| 2840 | " <a list of 10 Patch objects>)" |
||
| 2841 | ] |
||
| 2842 | }, |
||
| 2843 | "execution_count": 101, |
||
| 2844 | "metadata": {}, |
||
| 2845 | "output_type": "execute_result" |
||
| 2846 | }, |
||
| 2847 | { |
||
| 2848 | "data": { |
||
| 2849 | "image/png": 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\n", |
||
| 2850 | "text/plain": [ |
||
| 2851 | "<Figure size 720x720 with 1 Axes>" |
||
| 2852 | ] |
||
| 2853 | }, |
||
| 2854 | "metadata": { |
||
| 2855 | "needs_background": "light" |
||
| 2856 | }, |
||
| 2857 | "output_type": "display_data" |
||
| 2858 | } |
||
| 2859 | ], |
||
| 2860 | "source": [ |
||
| 2861 | "lo_shape_keys = [(i) for i in lo_outlier_shape.keys()]\n", |
||
| 2862 | "lo_shape_keys = np.array(lo_shape_keys)\n", |
||
| 2863 | "plt.hist(lo_shape_keys[:,1])" |
||
| 2864 | ] |
||
| 2865 | }, |
||
| 2866 | { |
||
| 2867 | "cell_type": "code", |
||
| 2868 | "execution_count": 103, |
||
| 2869 | "metadata": {}, |
||
| 2870 | "outputs": [ |
||
| 2871 | { |
||
| 2872 | "data": { |
||
| 2873 | "text/plain": [ |
||
| 2874 | "40" |
||
| 2875 | ] |
||
| 2876 | }, |
||
| 2877 | "execution_count": 103, |
||
| 2878 | "metadata": {}, |
||
| 2879 | "output_type": "execute_result" |
||
| 2880 | } |
||
| 2881 | ], |
||
| 2882 | "source": [ |
||
| 2883 | "len(lo_outlier_shape.keys())" |
||
| 2884 | ] |
||
| 2885 | }, |
||
| 2886 | { |
||
| 2887 | "cell_type": "code", |
||
| 2888 | "execution_count": 104, |
||
| 2889 | "metadata": {}, |
||
| 2890 | "outputs": [ |
||
| 2891 | { |
||
| 2892 | "data": { |
||
| 2893 | "text/plain": [ |
||
| 2894 | "{'[[1. 0. 0. 1.]\\n [0. 1. 0. 1.]\\n [0. 0. 1. 1.]\\n [0. 0. 0. 1.]]': ['/data/TrainingSet/labels/hippocampus_289.nii.gz',\n", |
||
| 2895 | " '/data/TrainingSet/labels/hippocampus_142.nii.gz',\n", |
||
| 2896 | " '/data/TrainingSet/labels/hippocampus_097.nii.gz',\n", |
||
| 2897 | " '/data/TrainingSet/labels/hippocampus_333.nii.gz',\n", |
||
| 2898 | " '/data/TrainingSet/labels/hippocampus_279.nii.gz',\n", |
||
| 2899 | " '/data/TrainingSet/labels/hippocampus_205.nii.gz',\n", |
||
| 2900 | " '/data/TrainingSet/labels/hippocampus_225.nii.gz',\n", |
||
| 2901 | " '/data/TrainingSet/labels/hippocampus_336.nii.gz',\n", |
||
| 2902 | " '/data/TrainingSet/labels/hippocampus_057.nii.gz',\n", |
||
| 2903 | " '/data/TrainingSet/labels/hippocampus_341.nii.gz',\n", |
||
| 2904 | " '/data/TrainingSet/labels/hippocampus_378.nii.gz',\n", |
||
| 2905 | " '/data/TrainingSet/labels/hippocampus_138.nii.gz',\n", |
||
| 2906 | " '/data/TrainingSet/labels/hippocampus_334.nii.gz',\n", |
||
| 2907 | " '/data/TrainingSet/labels/hippocampus_282.nii.gz',\n", |
||
| 2908 | " '/data/TrainingSet/labels/hippocampus_226.nii.gz',\n", |
||
| 2909 | " '/data/TrainingSet/labels/hippocampus_099.nii.gz',\n", |
||
| 2910 | " '/data/TrainingSet/labels/hippocampus_280.nii.gz',\n", |
||
| 2911 | " '/data/TrainingSet/labels/hippocampus_135.nii.gz',\n", |
||
| 2912 | " '/data/TrainingSet/labels/hippocampus_175.nii.gz',\n", |
||
| 2913 | " '/data/TrainingSet/labels/hippocampus_144.nii.gz',\n", |
||
| 2914 | " '/data/TrainingSet/labels/hippocampus_231.nii.gz',\n", |
||
| 2915 | " '/data/TrainingSet/labels/hippocampus_178.nii.gz',\n", |
||
| 2916 | " '/data/TrainingSet/labels/hippocampus_193.nii.gz',\n", |
||
| 2917 | " '/data/TrainingSet/labels/hippocampus_343.nii.gz',\n", |
||
| 2918 | " '/data/TrainingSet/labels/hippocampus_125.nii.gz',\n", |
||
| 2919 | " '/data/TrainingSet/labels/hippocampus_143.nii.gz',\n", |
||
| 2920 | " '/data/TrainingSet/labels/hippocampus_353.nii.gz',\n", |
||
| 2921 | " '/data/TrainingSet/labels/hippocampus_335.nii.gz',\n", |
||
| 2922 | " '/data/TrainingSet/labels/hippocampus_221.nii.gz',\n", |
||
| 2923 | " '/data/TrainingSet/labels/hippocampus_320.nii.gz',\n", |
||
| 2924 | " '/data/TrainingSet/labels/hippocampus_274.nii.gz',\n", |
||
| 2925 | " '/data/TrainingSet/labels/hippocampus_297.nii.gz',\n", |
||
| 2926 | " '/data/TrainingSet/labels/hippocampus_319.nii.gz',\n", |
||
| 2927 | " '/data/TrainingSet/labels/hippocampus_180.nii.gz',\n", |
||
| 2928 | " '/data/TrainingSet/labels/hippocampus_349.nii.gz',\n", |
||
| 2929 | " '/data/TrainingSet/labels/hippocampus_222.nii.gz',\n", |
||
| 2930 | " '/data/TrainingSet/labels/hippocampus_177.nii.gz',\n", |
||
| 2931 | " '/data/TrainingSet/labels/hippocampus_359.nii.gz',\n", |
||
| 2932 | " '/data/TrainingSet/labels/hippocampus_141.nii.gz'],\n", |
||
| 2933 | " '[[1. 0. 0. 0.]\\n [0. 1. 0. 0.]\\n [0. 0. 1. 0.]\\n [0. 0. 0. 1.]]': ['/data/TrainingSet/labels/hippocampus_199.nii.gz']}" |
||
| 2934 | ] |
||
| 2935 | }, |
||
| 2936 | "execution_count": 104, |
||
| 2937 | "metadata": {}, |
||
| 2938 | "output_type": "execute_result" |
||
| 2939 | } |
||
| 2940 | ], |
||
| 2941 | "source": [ |
||
| 2942 | "lo_outlier_sform" |
||
| 2943 | ] |
||
| 2944 | }, |
||
| 2945 | { |
||
| 2946 | "cell_type": "code", |
||
| 2947 | "execution_count": 105, |
||
| 2948 | "metadata": {}, |
||
| 2949 | "outputs": [ |
||
| 2950 | { |
||
| 2951 | "data": { |
||
| 2952 | "text/plain": [ |
||
| 2953 | "<matplotlib.image.AxesImage at 0x7f3a6fd44df0>" |
||
| 2954 | ] |
||
| 2955 | }, |
||
| 2956 | "execution_count": 105, |
||
| 2957 | "metadata": {}, |
||
| 2958 | "output_type": "execute_result" |
||
| 2959 | }, |
||
| 2960 | { |
||
| 2961 | "data": { |
||
| 2962 | "image/png": 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\n", |
||
| 2963 | "text/plain": [ |
||
| 2964 | "<Figure size 720x720 with 2 Axes>" |
||
| 2965 | ] |
||
| 2966 | }, |
||
| 2967 | "metadata": { |
||
| 2968 | "needs_background": "light" |
||
| 2969 | }, |
||
| 2970 | "output_type": "display_data" |
||
| 2971 | } |
||
| 2972 | ], |
||
| 2973 | "source": [ |
||
| 2974 | "plt.subplots(1,2)\n", |
||
| 2975 | "plt.subplot(1,2,1)\n", |
||
| 2976 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_279.nii.gz').get_fdata()[15,:,:])\n", |
||
| 2977 | "plt.subplot(1,2,2)\n", |
||
| 2978 | "plt.imshow(nib.load('/data/TrainingSet/labels/hippocampus_279.nii.gz').get_fdata()[15,:,:])" |
||
| 2979 | ] |
||
| 2980 | }, |
||
| 2981 | { |
||
| 2982 | "cell_type": "code", |
||
| 2983 | "execution_count": 106, |
||
| 2984 | "metadata": {}, |
||
| 2985 | "outputs": [ |
||
| 2986 | { |
||
| 2987 | "data": { |
||
| 2988 | "text/plain": [ |
||
| 2989 | "{'8': ['/data/TrainingSet/labels/hippocampus_289.nii.gz',\n", |
||
| 2990 | " '/data/TrainingSet/labels/hippocampus_142.nii.gz',\n", |
||
| 2991 | " '/data/TrainingSet/labels/hippocampus_097.nii.gz',\n", |
||
| 2992 | " '/data/TrainingSet/labels/hippocampus_333.nii.gz',\n", |
||
| 2993 | " '/data/TrainingSet/labels/hippocampus_279.nii.gz',\n", |
||
| 2994 | " '/data/TrainingSet/labels/hippocampus_205.nii.gz',\n", |
||
| 2995 | " '/data/TrainingSet/labels/hippocampus_225.nii.gz',\n", |
||
| 2996 | " '/data/TrainingSet/labels/hippocampus_336.nii.gz',\n", |
||
| 2997 | " '/data/TrainingSet/labels/hippocampus_057.nii.gz',\n", |
||
| 2998 | " '/data/TrainingSet/labels/hippocampus_341.nii.gz',\n", |
||
| 2999 | " '/data/TrainingSet/labels/hippocampus_378.nii.gz',\n", |
||
| 3000 | " '/data/TrainingSet/labels/hippocampus_138.nii.gz',\n", |
||
| 3001 | " '/data/TrainingSet/labels/hippocampus_334.nii.gz',\n", |
||
| 3002 | " '/data/TrainingSet/labels/hippocampus_282.nii.gz',\n", |
||
| 3003 | " '/data/TrainingSet/labels/hippocampus_226.nii.gz',\n", |
||
| 3004 | " '/data/TrainingSet/labels/hippocampus_099.nii.gz',\n", |
||
| 3005 | " '/data/TrainingSet/labels/hippocampus_199.nii.gz',\n", |
||
| 3006 | " '/data/TrainingSet/labels/hippocampus_280.nii.gz',\n", |
||
| 3007 | " '/data/TrainingSet/labels/hippocampus_135.nii.gz',\n", |
||
| 3008 | " '/data/TrainingSet/labels/hippocampus_175.nii.gz',\n", |
||
| 3009 | " '/data/TrainingSet/labels/hippocampus_144.nii.gz',\n", |
||
| 3010 | " '/data/TrainingSet/labels/hippocampus_231.nii.gz',\n", |
||
| 3011 | " '/data/TrainingSet/labels/hippocampus_178.nii.gz',\n", |
||
| 3012 | " '/data/TrainingSet/labels/hippocampus_193.nii.gz',\n", |
||
| 3013 | " '/data/TrainingSet/labels/hippocampus_343.nii.gz',\n", |
||
| 3014 | " '/data/TrainingSet/labels/hippocampus_125.nii.gz',\n", |
||
| 3015 | " '/data/TrainingSet/labels/hippocampus_143.nii.gz',\n", |
||
| 3016 | " '/data/TrainingSet/labels/hippocampus_353.nii.gz',\n", |
||
| 3017 | " '/data/TrainingSet/labels/hippocampus_335.nii.gz',\n", |
||
| 3018 | " '/data/TrainingSet/labels/hippocampus_221.nii.gz',\n", |
||
| 3019 | " '/data/TrainingSet/labels/hippocampus_320.nii.gz',\n", |
||
| 3020 | " '/data/TrainingSet/labels/hippocampus_274.nii.gz',\n", |
||
| 3021 | " '/data/TrainingSet/labels/hippocampus_297.nii.gz',\n", |
||
| 3022 | " '/data/TrainingSet/labels/hippocampus_319.nii.gz',\n", |
||
| 3023 | " '/data/TrainingSet/labels/hippocampus_180.nii.gz',\n", |
||
| 3024 | " '/data/TrainingSet/labels/hippocampus_349.nii.gz',\n", |
||
| 3025 | " '/data/TrainingSet/labels/hippocampus_222.nii.gz',\n", |
||
| 3026 | " '/data/TrainingSet/labels/hippocampus_177.nii.gz',\n", |
||
| 3027 | " '/data/TrainingSet/labels/hippocampus_359.nii.gz',\n", |
||
| 3028 | " '/data/TrainingSet/labels/hippocampus_141.nii.gz']}" |
||
| 3029 | ] |
||
| 3030 | }, |
||
| 3031 | "execution_count": 106, |
||
| 3032 | "metadata": {}, |
||
| 3033 | "output_type": "execute_result" |
||
| 3034 | } |
||
| 3035 | ], |
||
| 3036 | "source": [ |
||
| 3037 | "lo_outlier_bitpix" |
||
| 3038 | ] |
||
| 3039 | }, |
||
| 3040 | { |
||
| 3041 | "cell_type": "markdown", |
||
| 3042 | "metadata": {}, |
||
| 3043 | "source": [ |
||
| 3044 | "All data has bitpix of 8." |
||
| 3045 | ] |
||
| 3046 | }, |
||
| 3047 | { |
||
| 3048 | "cell_type": "code", |
||
| 3049 | "execution_count": 107, |
||
| 3050 | "metadata": {}, |
||
| 3051 | "outputs": [ |
||
| 3052 | { |
||
| 3053 | "data": { |
||
| 3054 | "text/plain": [ |
||
| 3055 | "<function matplotlib.pyplot.show(*args, **kw)>" |
||
| 3056 | ] |
||
| 3057 | }, |
||
| 3058 | "execution_count": 107, |
||
| 3059 | "metadata": {}, |
||
| 3060 | "output_type": "execute_result" |
||
| 3061 | }, |
||
| 3062 | { |
||
| 3063 | "data": { |
||
| 3064 | "image/png": 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\n", |
||
| 3065 | "text/plain": [ |
||
| 3066 | "<Figure size 1080x1080 with 1 Axes>" |
||
| 3067 | ] |
||
| 3068 | }, |
||
| 3069 | "metadata": { |
||
| 3070 | "needs_background": "light" |
||
| 3071 | }, |
||
| 3072 | "output_type": "display_data" |
||
| 3073 | } |
||
| 3074 | ], |
||
| 3075 | "source": [ |
||
| 3076 | "plt.figure(figsize=(15,15))\n", |
||
| 3077 | "plt.xlim(0,5000)\n", |
||
| 3078 | "plt.hist(train_set_volumes, bins = 1000)\n", |
||
| 3079 | "plt.axvline(2200,color='r')\n", |
||
| 3080 | "plt.show" |
||
| 3081 | ] |
||
| 3082 | }, |
||
| 3083 | { |
||
| 3084 | "cell_type": "markdown", |
||
| 3085 | "metadata": {}, |
||
| 3086 | "source": [ |
||
| 3087 | "### Did not identifier one discernable outlier in the low hippocampus volume set." |
||
| 3088 | ] |
||
| 3089 | }, |
||
| 3090 | { |
||
| 3091 | "cell_type": "markdown", |
||
| 3092 | "metadata": {}, |
||
| 3093 | "source": [ |
||
| 3094 | "### Check that all remaining labels and images have the same dimensions" |
||
| 3095 | ] |
||
| 3096 | }, |
||
| 3097 | { |
||
| 3098 | "cell_type": "code", |
||
| 3099 | "execution_count": 135, |
||
| 3100 | "metadata": {}, |
||
| 3101 | "outputs": [], |
||
| 3102 | "source": [ |
||
| 3103 | "no_outlier2 = np.concatenate((no_outlier,lo_outlier))" |
||
| 3104 | ] |
||
| 3105 | }, |
||
| 3106 | { |
||
| 3107 | "cell_type": "code", |
||
| 3108 | "execution_count": 138, |
||
| 3109 | "metadata": {}, |
||
| 3110 | "outputs": [], |
||
| 3111 | "source": [ |
||
| 3112 | "difference = []\n", |
||
| 3113 | "no_outlier2[0][1]\n", |
||
| 3114 | "for i in no_outlier2:\n", |
||
| 3115 | " label_p = '/data/TrainingSet/labels/'\n", |
||
| 3116 | " images_p = '/data/TrainingSet/images/'\n", |
||
| 3117 | " fN = i[1].split('/')[4]\n", |
||
| 3118 | " delta = nib.load(label_p+fN).header['dim'] - nib.load(images_p + fN).header['dim']\n", |
||
| 3119 | " difference.append([fN, delta])" |
||
| 3120 | ] |
||
| 3121 | }, |
||
| 3122 | { |
||
| 3123 | "cell_type": "code", |
||
| 3124 | "execution_count": 139, |
||
| 3125 | "metadata": {}, |
||
| 3126 | "outputs": [ |
||
| 3127 | { |
||
| 3128 | "data": { |
||
| 3129 | "text/plain": [ |
||
| 3130 | "[['hippocampus_376.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
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| 3131 | " ['hippocampus_165.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
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| 3132 | " ['hippocampus_286.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
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| 3133 | " ['hippocampus_152.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3134 | " ['hippocampus_176.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3135 | " ['hippocampus_096.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3136 | " ['hippocampus_068.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3137 | " ['hippocampus_260.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3138 | " ['hippocampus_171.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3139 | " ['hippocampus_296.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3140 | " ['hippocampus_375.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3141 | " ['hippocampus_064.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3142 | " ['hippocampus_108.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3143 | " ['hippocampus_070.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3144 | " ['hippocampus_329.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3145 | " ['hippocampus_107.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3146 | " ['hippocampus_090.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3147 | " ['hippocampus_092.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3148 | " ['hippocampus_318.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3149 | " ['hippocampus_123.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3150 | " ['hippocampus_037.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3151 | " ['hippocampus_023.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3152 | " ['hippocampus_330.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3153 | " ['hippocampus_374.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3154 | " ['hippocampus_380.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3155 | " ['hippocampus_056.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3156 | " ['hippocampus_040.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3157 | " ['hippocampus_127.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3158 | " ['hippocampus_163.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3159 | " ['hippocampus_146.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3160 | " ['hippocampus_050.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3161 | " ['hippocampus_224.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3162 | " ['hippocampus_301.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3163 | " ['hippocampus_160.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3164 | " ['hippocampus_352.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3165 | " ['hippocampus_114.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3166 | " ['hippocampus_089.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3167 | " ['hippocampus_026.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3168 | " ['hippocampus_295.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3169 | " ['hippocampus_045.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3170 | " ['hippocampus_390.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3171 | " ['hippocampus_075.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3172 | " ['hippocampus_197.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3173 | " ['hippocampus_087.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3174 | " ['hippocampus_370.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3175 | " ['hippocampus_145.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3176 | " ['hippocampus_035.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3177 | " ['hippocampus_195.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3178 | " ['hippocampus_385.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3179 | " ['hippocampus_189.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3180 | " ['hippocampus_060.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3181 | " ['hippocampus_132.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3182 | " ['hippocampus_361.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3183 | " ['hippocampus_174.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3184 | " ['hippocampus_150.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3185 | " ['hippocampus_094.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3186 | " ['hippocampus_316.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3187 | " ['hippocampus_345.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3188 | " ['hippocampus_308.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3189 | " ['hippocampus_250.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3190 | " ['hippocampus_245.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3191 | " ['hippocampus_105.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3192 | " ['hippocampus_039.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3193 | " ['hippocampus_372.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3194 | " ['hippocampus_003.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3195 | " ['hippocampus_259.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3196 | " ['hippocampus_340.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3197 | " ['hippocampus_219.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3198 | " ['hippocampus_302.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3199 | " ['hippocampus_366.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3200 | " ['hippocampus_025.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3201 | " ['hippocampus_019.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3202 | " ['hippocampus_109.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3203 | " ['hippocampus_299.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3204 | " ['hippocampus_155.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3205 | " ['hippocampus_381.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3206 | " ['hippocampus_294.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3207 | " ['hippocampus_033.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3208 | " ['hippocampus_373.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3209 | " ['hippocampus_232.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3210 | " ['hippocampus_166.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3211 | " ['hippocampus_305.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3212 | " ['hippocampus_233.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3213 | " ['hippocampus_249.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3214 | " ['hippocampus_203.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3215 | " ['hippocampus_393.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3216 | " ['hippocampus_251.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3217 | " ['hippocampus_287.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3218 | " ['hippocampus_126.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3219 | " ['hippocampus_130.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3220 | " ['hippocampus_158.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3221 | " ['hippocampus_149.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3222 | " ['hippocampus_190.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3223 | " ['hippocampus_084.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3224 | " ['hippocampus_269.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3225 | " ['hippocampus_257.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3226 | " ['hippocampus_067.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3227 | " ['hippocampus_095.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3228 | " ['hippocampus_298.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3229 | " ['hippocampus_387.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3230 | " ['hippocampus_252.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3231 | " ['hippocampus_106.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3232 | " ['hippocampus_004.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3233 | " ['hippocampus_235.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3234 | " ['hippocampus_194.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3235 | " ['hippocampus_093.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3236 | " ['hippocampus_017.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3237 | " ['hippocampus_367.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3238 | " ['hippocampus_185.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3239 | " ['hippocampus_321.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3240 | " ['hippocampus_024.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3241 | " ['hippocampus_041.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3242 | " ['hippocampus_162.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3243 | " ['hippocampus_243.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3244 | " ['hippocampus_338.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3245 | " ['hippocampus_048.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3246 | " ['hippocampus_331.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3247 | " ['hippocampus_217.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3248 | " ['hippocampus_228.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3249 | " ['hippocampus_337.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3250 | " ['hippocampus_368.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3251 | " ['hippocampus_058.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3252 | " ['hippocampus_014.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3253 | " ['hippocampus_394.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3254 | " ['hippocampus_088.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3255 | " ['hippocampus_309.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3256 | " ['hippocampus_091.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3257 | " ['hippocampus_036.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3258 | " ['hippocampus_007.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3259 | " ['hippocampus_288.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3260 | " ['hippocampus_216.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3261 | " ['hippocampus_248.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3262 | " ['hippocampus_227.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3263 | " ['hippocampus_332.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3264 | " ['hippocampus_234.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3265 | " ['hippocampus_052.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3266 | " ['hippocampus_169.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3267 | " ['hippocampus_020.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3268 | " ['hippocampus_265.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3269 | " ['hippocampus_212.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3270 | " ['hippocampus_133.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3271 | " ['hippocampus_006.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3272 | " ['hippocampus_015.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3273 | " ['hippocampus_360.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3274 | " ['hippocampus_322.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3275 | " ['hippocampus_101.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3276 | " ['hippocampus_311.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3277 | " ['hippocampus_238.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3278 | " ['hippocampus_001.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3279 | " ['hippocampus_261.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3280 | " ['hippocampus_207.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3281 | " ['hippocampus_184.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3282 | " ['hippocampus_044.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3283 | " ['hippocampus_386.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3284 | " ['hippocampus_290.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3285 | " ['hippocampus_310.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3286 | " ['hippocampus_148.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3287 | " ['hippocampus_264.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3288 | " ['hippocampus_358.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3289 | " ['hippocampus_276.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3290 | " ['hippocampus_363.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3291 | " ['hippocampus_204.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3292 | " ['hippocampus_389.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3293 | " ['hippocampus_230.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3294 | " ['hippocampus_350.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3295 | " ['hippocampus_051.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3296 | " ['hippocampus_304.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3297 | " ['hippocampus_188.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3298 | " ['hippocampus_065.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3299 | " ['hippocampus_104.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3300 | " ['hippocampus_351.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3301 | " ['hippocampus_008.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3302 | " ['hippocampus_317.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3303 | " ['hippocampus_236.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3304 | " ['hippocampus_170.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3305 | " ['hippocampus_077.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3306 | " ['hippocampus_173.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3307 | " ['hippocampus_049.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3308 | " ['hippocampus_292.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3309 | " ['hippocampus_229.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3310 | " ['hippocampus_253.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3311 | " ['hippocampus_263.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3312 | " ['hippocampus_314.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3313 | " ['hippocampus_355.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3314 | " ['hippocampus_011.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3315 | " ['hippocampus_098.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3316 | " ['hippocampus_161.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3317 | " ['hippocampus_102.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3318 | " ['hippocampus_154.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3319 | " ['hippocampus_157.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3320 | " ['hippocampus_327.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3321 | " ['hippocampus_215.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3322 | " ['hippocampus_074.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3323 | " ['hippocampus_242.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3324 | " ['hippocampus_046.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3325 | " ['hippocampus_034.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3326 | " ['hippocampus_223.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3327 | " ['hippocampus_328.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3328 | " ['hippocampus_038.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3329 | " ['hippocampus_220.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3330 | " ['hippocampus_383.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3331 | " ['hippocampus_244.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3332 | " ['hippocampus_277.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3333 | " ['hippocampus_083.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3334 | " ['hippocampus_010.nii.gz',\n", |
||
| 3335 | " array([ 0, -476, -462, -210, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3336 | " ['hippocampus_042.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3337 | " ['hippocampus_300.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3338 | " ['hippocampus_164.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3339 | " ['hippocampus_303.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3340 | " ['hippocampus_326.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3341 | " ['hippocampus_124.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3342 | " ['hippocampus_268.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3343 | " ['hippocampus_053.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3344 | " ['hippocampus_172.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3345 | " ['hippocampus_156.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3346 | " ['hippocampus_136.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3347 | " ['hippocampus_181.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3348 | " ['hippocampus_354.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3349 | " ['hippocampus_356.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3350 | " ['hippocampus_325.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3351 | " ['hippocampus_210.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3352 | " ['hippocampus_289.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3353 | " ['hippocampus_142.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3354 | " ['hippocampus_097.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3355 | " ['hippocampus_333.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3356 | " ['hippocampus_279.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3357 | " ['hippocampus_205.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3358 | " ['hippocampus_225.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3359 | " ['hippocampus_336.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3360 | " ['hippocampus_057.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3361 | " ['hippocampus_341.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3362 | " ['hippocampus_378.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3363 | " ['hippocampus_138.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3364 | " ['hippocampus_334.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3365 | " ['hippocampus_282.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3366 | " ['hippocampus_226.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3367 | " ['hippocampus_099.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3368 | " ['hippocampus_199.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3369 | " ['hippocampus_280.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3370 | " ['hippocampus_135.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3371 | " ['hippocampus_175.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3372 | " ['hippocampus_144.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3373 | " ['hippocampus_231.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3374 | " ['hippocampus_178.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3375 | " ['hippocampus_193.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3376 | " ['hippocampus_343.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3377 | " ['hippocampus_125.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3378 | " ['hippocampus_143.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3379 | " ['hippocampus_353.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3380 | " ['hippocampus_335.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3381 | " ['hippocampus_221.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3382 | " ['hippocampus_320.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3383 | " ['hippocampus_274.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3384 | " ['hippocampus_297.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3385 | " ['hippocampus_319.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3386 | " ['hippocampus_180.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3387 | " ['hippocampus_349.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3388 | " ['hippocampus_222.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3389 | " ['hippocampus_177.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3390 | " ['hippocampus_359.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)],\n", |
||
| 3391 | " ['hippocampus_141.nii.gz', array([0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)]]" |
||
| 3392 | ] |
||
| 3393 | }, |
||
| 3394 | "execution_count": 139, |
||
| 3395 | "metadata": {}, |
||
| 3396 | "output_type": "execute_result" |
||
| 3397 | } |
||
| 3398 | ], |
||
| 3399 | "source": [ |
||
| 3400 | "difference" |
||
| 3401 | ] |
||
| 3402 | }, |
||
| 3403 | { |
||
| 3404 | "cell_type": "markdown", |
||
| 3405 | "metadata": {}, |
||
| 3406 | "source": [ |
||
| 3407 | "Found a second outlier!\n", |
||
| 3408 | "NIFTI file 'hippocampus_010.nii.gz' has a mismatch in the dimensions of its mask and its image." |
||
| 3409 | ] |
||
| 3410 | }, |
||
| 3411 | { |
||
| 3412 | "cell_type": "code", |
||
| 3413 | "execution_count": 144, |
||
| 3414 | "metadata": {}, |
||
| 3415 | "outputs": [ |
||
| 3416 | { |
||
| 3417 | "data": { |
||
| 3418 | "text/plain": [ |
||
| 3419 | "<matplotlib.image.AxesImage at 0x7f3a741f1c40>" |
||
| 3420 | ] |
||
| 3421 | }, |
||
| 3422 | "execution_count": 144, |
||
| 3423 | "metadata": {}, |
||
| 3424 | "output_type": "execute_result" |
||
| 3425 | }, |
||
| 3426 | { |
||
| 3427 | "data": { |
||
| 3428 | "image/png": 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\n", |
||
| 3429 | "text/plain": [ |
||
| 3430 | "<Figure size 720x720 with 2 Axes>" |
||
| 3431 | ] |
||
| 3432 | }, |
||
| 3433 | "metadata": { |
||
| 3434 | "needs_background": "light" |
||
| 3435 | }, |
||
| 3436 | "output_type": "display_data" |
||
| 3437 | } |
||
| 3438 | ], |
||
| 3439 | "source": [ |
||
| 3440 | "plt.subplots(1,2)\n", |
||
| 3441 | "plt.subplot(1,2,1)\n", |
||
| 3442 | "plt.imshow(nib.load('/data/TrainingSet/images/hippocampus_010.nii.gz').get_fdata()[16,:,:])\n", |
||
| 3443 | "plt.title('Image')\n", |
||
| 3444 | "plt.subplot(1,2,2)\n", |
||
| 3445 | "plt.title('Label')\n", |
||
| 3446 | "plt.imshow(nib.load('/data/TrainingSet/labels/hippocampus_010.nii.gz').get_fdata()[16,:,:])" |
||
| 3447 | ] |
||
| 3448 | }, |
||
| 3449 | { |
||
| 3450 | "cell_type": "markdown", |
||
| 3451 | "metadata": {}, |
||
| 3452 | "source": [ |
||
| 3453 | "The Image looks odd and doesn't match the label. The label may represent a Hippocampus, but it has no corresponding image to train to. Hence this file must be dropped 'hippocampus_010.nii.gz'" |
||
| 3454 | ] |
||
| 3455 | }, |
||
| 3456 | { |
||
| 3457 | "cell_type": "code", |
||
| 3458 | "execution_count": 162, |
||
| 3459 | "metadata": {}, |
||
| 3460 | "outputs": [], |
||
| 3461 | "source": [ |
||
| 3462 | "no_outlier2 = no_outlier2[no_outlier2[:,1]!='/data/TrainingSet/labels/hippocampus_010.nii.gz']" |
||
| 3463 | ] |
||
| 3464 | }, |
||
| 3465 | { |
||
| 3466 | "cell_type": "code", |
||
| 3467 | "execution_count": 163, |
||
| 3468 | "metadata": {}, |
||
| 3469 | "outputs": [ |
||
| 3470 | { |
||
| 3471 | "data": { |
||
| 3472 | "text/plain": [ |
||
| 3473 | "(260, 3)" |
||
| 3474 | ] |
||
| 3475 | }, |
||
| 3476 | "execution_count": 163, |
||
| 3477 | "metadata": {}, |
||
| 3478 | "output_type": "execute_result" |
||
| 3479 | } |
||
| 3480 | ], |
||
| 3481 | "source": [ |
||
| 3482 | "no_outlier2.shape" |
||
| 3483 | ] |
||
| 3484 | }, |
||
| 3485 | { |
||
| 3486 | "cell_type": "code", |
||
| 3487 | "execution_count": 164, |
||
| 3488 | "metadata": {}, |
||
| 3489 | "outputs": [ |
||
| 3490 | { |
||
| 3491 | "data": { |
||
| 3492 | "text/plain": [ |
||
| 3493 | "array([False, False, False, False, False, False, False, False, False,\n", |
||
| 3494 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3495 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3496 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3497 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3498 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3499 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3500 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3501 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3502 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3503 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3504 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3505 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3506 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3507 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3508 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3509 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3510 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3511 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3512 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3513 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3514 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3515 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3516 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3517 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3518 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3519 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3520 | " False, False, False, False, False, False, False, False, False,\n", |
||
| 3521 | " False, False, False, False, False, False, False, False])" |
||
| 3522 | ] |
||
| 3523 | }, |
||
| 3524 | "execution_count": 164, |
||
| 3525 | "metadata": {}, |
||
| 3526 | "output_type": "execute_result" |
||
| 3527 | } |
||
| 3528 | ], |
||
| 3529 | "source": [ |
||
| 3530 | "no_outlier2[:,1]=='/data/TrainingSet/labels/hippocampus_010.nii.gz'" |
||
| 3531 | ] |
||
| 3532 | }, |
||
| 3533 | { |
||
| 3534 | "cell_type": "markdown", |
||
| 3535 | "metadata": {}, |
||
| 3536 | "source": [ |
||
| 3537 | "## Identified outlier files: hippocampus_010.nii.gz and hippocampus_281.nii.gz" |
||
| 3538 | ] |
||
| 3539 | }, |
||
| 3540 | { |
||
| 3541 | "cell_type": "markdown", |
||
| 3542 | "metadata": {}, |
||
| 3543 | "source": [ |
||
| 3544 | "In the real world we would have precise information about the ages and conditions of our patients, and understanding how our dataset measures against population norm would be the integral part of clinical validation that we talked about in last lesson. Unfortunately, we do not have this information about this dataset, so we can only guess why it measures the way it is. If you would like to explore further, you can use the [calculator from HippoFit project](http://www.smanohar.com/biobank/calculator.html) to see how our dataset compares against different population slices" |
||
| 3545 | ] |
||
| 3546 | }, |
||
| 3547 | { |
||
| 3548 | "cell_type": "markdown", |
||
| 3549 | "metadata": {}, |
||
| 3550 | "source": [ |
||
| 3551 | "Did you notice anything odd about the label files? We hope you did! The mask seems to have two classes, labeled with values `1` and `2` respectively. If you visualized sagittal or axial views, you might have gotten a good guess of what those are. Class 1 is the anterior segment of the hippocampus and class 2 is the posterior one. \n", |
||
| 3552 | "\n", |
||
| 3553 | "For the purpose of volume calculation we do not care about the distinction, however we will still train our network to differentiate between these two classes and the background" |
||
| 3554 | ] |
||
| 3555 | }, |
||
| 3556 | { |
||
| 3557 | "cell_type": "code", |
||
| 3558 | "execution_count": 166, |
||
| 3559 | "metadata": {}, |
||
| 3560 | "outputs": [ |
||
| 3561 | { |
||
| 3562 | "name": "stdout", |
||
| 3563 | "output_type": "stream", |
||
| 3564 | "text": [ |
||
| 3565 | "/data/TrainingSet/labels/hippocampus_376.nii.gz\n", |
||
| 3566 | "/data/TrainingSet/labels/hippocampus_165.nii.gz\n", |
||
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| 3821 | "/data/TrainingSet/labels/hippocampus_222.nii.gz\n", |
||
| 3822 | "/data/TrainingSet/labels/hippocampus_177.nii.gz\n", |
||
| 3823 | "/data/TrainingSet/labels/hippocampus_359.nii.gz\n", |
||
| 3824 | "/data/TrainingSet/labels/hippocampus_141.nii.gz\n" |
||
| 3825 | ] |
||
| 3826 | } |
||
| 3827 | ], |
||
| 3828 | "source": [ |
||
| 3829 | "# TASK: Copy the clean dataset to the output folder inside section1/out. You will use it in the next Section\n", |
||
| 3830 | "count=0\n", |
||
| 3831 | "for f in no_outlier2:\n", |
||
| 3832 | " print(f[1])\n", |
||
| 3833 | " fn = f[1].split('/')[4]\n", |
||
| 3834 | " shutil.copy(f[1], f'out/labels/{fn}')\n", |
||
| 3835 | " shutil.copy(f'/data/TrainingSet/images/{fn}', f'out/images/{fn}')" |
||
| 3836 | ] |
||
| 3837 | }, |
||
| 3838 | { |
||
| 3839 | "cell_type": "markdown", |
||
| 3840 | "metadata": {}, |
||
| 3841 | "source": [ |
||
| 3842 | "## Final remarks\n", |
||
| 3843 | "\n", |
||
| 3844 | "Congratulations! You have finished Section 1. \n", |
||
| 3845 | "\n", |
||
| 3846 | "In this section you have inspected a dataset of MRI scans and related segmentations, represented as NIFTI files. We have visualized some slices, and understood the layout of the data. We have inspected file headers to understand what how the image dimensions relate to the physical world and we have understood how to measure our volume. We have then inspected dataset for outliers, and have created a clean set that is ready for consumption by our ML algorithm. \n", |
||
| 3847 | "\n", |
||
| 3848 | "In the next section you will create training and testing pipelines for a UNet-based machine learning model, run and monitor the execution, and will produce test metrics. This will arm you with all you need to use the model in the clinical context and reason about its performance!" |
||
| 3849 | ] |
||
| 3850 | } |
||
| 3851 | ], |
||
| 3852 | "metadata": { |
||
| 3853 | "kernelspec": { |
||
| 3854 | "display_name": "Python 3", |
||
| 3855 | "language": "python", |
||
| 3856 | "name": "python3" |
||
| 3857 | }, |
||
| 3858 | "language_info": { |
||
| 3859 | "codemirror_mode": { |
||
| 3860 | "name": "ipython", |
||
| 3861 | "version": 3 |
||
| 3862 | }, |
||
| 3863 | "file_extension": ".py", |
||
| 3864 | "mimetype": "text/x-python", |
||
| 3865 | "name": "python", |
||
| 3866 | "nbconvert_exporter": "python", |
||
| 3867 | "pygments_lexer": "ipython3", |
||
| 3868 | "version": "3.8.2" |
||
| 3869 | } |
||
| 3870 | }, |
||
| 3871 | "nbformat": 4, |
||
| 3872 | "nbformat_minor": 2 |
||
| 3873 | } |