Datasets
Models
Downloads: 1
Diff of
/MRNet_EDA_ns.ipynb
[000000]
..
[dc3c86]
Switch to side-by-side view
--- a +++ b/MRNet_EDA_ns.ipynb @@ -0,0 +1,1215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from fastai.core import *\n", + "\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LICENSE MRNet_EDA.ipynb README.md\r\n" + ] + } + ], + "source": [ + "! ls -R " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path('../data')\n", + "train_path = data_path/'smalltrain'/'train'\n", + "valid_path = data_path/'smallvalid'/'valid'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Case\n", + "Abnormal \n", + "0 217\n", + "1 913\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Case</th>\n", + " <th>Abnormal</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0000</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0001</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0002</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0003</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0004</td>\n", + " <td>1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Case Abnormal\n", + "0 0000 1\n", + "1 0001 1\n", + "2 0002 1\n", + "3 0003 1\n", + "4 0004 1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_abnl = pd.read_csv(data_path/'train-abnormal.csv', header=None,\n", + " names=['Case', 'Abnormal'], \n", + " dtype={'Case': str, 'Abnormal': np.int64})\n", + "print(train_abnl.groupby('Abnormal').count())\n", + "train_abnl.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Case\n", + "ACL_tear \n", + "0 922\n", + "1 208\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Case</th>\n", + " <th>ACL_tear</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0000</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0001</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0002</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0003</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0004</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Case ACL_tear\n", + "0 0000 0\n", + "1 0001 1\n", + "2 0002 0\n", + "3 0003 0\n", + "4 0004 0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_acl = pd.read_csv(data_path/'train-acl.csv', header=None,\n", + " names=['Case', 'ACL_tear'], \n", + " dtype={'Case': str, 'ACL_tear': np.int64})\n", + "print(train_acl.groupby('ACL_tear').count())\n", + "train_acl.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Case\n", + "Meniscus_tear \n", + "0 733\n", + "1 397\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Case</th>\n", + " <th>Meniscus_tear</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0000</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0001</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0002</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0003</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0004</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Case Meniscus_tear\n", + "0 0000 0\n", + "1 0001 1\n", + "2 0002 0\n", + "3 0003 1\n", + "4 0004 0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_meniscus = pd.read_csv(data_path/'train-meniscus.csv', header=None,\n", + " names=['Case', 'Meniscus_tear'], \n", + " dtype={'Case': str, 'Meniscus_tear': np.int64})\n", + "print(train_meniscus.groupby('Meniscus_tear').count())\n", + "train_meniscus.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Co-occurrence of ACL and Meniscus tears" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train = pd.merge(train_abnl, train_acl, on='Case')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "train = pd.merge(train, train_meniscus, on='Case')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Case</th>\n", + " <th>Abnormal</th>\n", + " <th>ACL_tear</th>\n", + " <th>Meniscus_tear</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0000</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0001</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0002</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0003</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0004</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Case Abnormal ACL_tear Meniscus_tear\n", + "0 0000 1 0 0\n", + "1 0001 1 1 1\n", + "2 0002 1 0 0\n", + "3 0003 1 0 1\n", + "4 0004 1 0 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th>Case</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Abnormal</th>\n", + " <th>ACL_tear</th>\n", + " <th>Meniscus_tear</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <th>0</th>\n", + " <th>0</th>\n", + " <td>217</td>\n", + " </tr>\n", + " <tr>\n", + " <th rowspan=\"4\" valign=\"top\">1</th>\n", + " <th rowspan=\"2\" valign=\"top\">0</th>\n", + " <th>0</th>\n", + " <td>433</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>272</td>\n", + " </tr>\n", + " <tr>\n", + " <th rowspan=\"2\" valign=\"top\">1</th>\n", + " <th>0</th>\n", + " <td>83</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>125</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Case\n", + "Abnormal ACL_tear Meniscus_tear \n", + "0 0 0 217\n", + "1 0 0 433\n", + " 1 272\n", + " 1 0 83\n", + " 1 125" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(train.head())\n", + "display(train.groupby(['Abnormal','ACL_tear','Meniscus_tear']).count())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that cases considered Abnormal but without either ACL or Meniscus tear are the most common category, and ACL tears without Meniscus tear is the least common case in the training sample." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load stacks/sequences of images from each plane\n", + "Files are saved as NumPy arrays. Scans were taken from each of three planes, axial, coronal, and sagittal. For each plane, the scan results in a set of images. \n", + "\n", + "First, let's check for variation in the number of images per sequence, and in the image dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "def collect_stack_dims(case_df, data_path=train_path):\n", + " cases = list(case_df.Case)\n", + " data = []\n", + " for case in cases:\n", + " row = [case]\n", + " for plane in ['axial', 'coronal', 'sagittal']:\n", + " fpath = data_path/plane/'{}.npy'.format(case)\n", + " try: \n", + " s,w,h = np.load(fpath).shape \n", + " row.extend([s,w,h])\n", + " except FileNotFoundError:\n", + " continue\n", + "# print('{}: {}'.format(case,row))\n", + " if len(row)==10: data.append(row)\n", + " columns=['Case',\n", + " 'axial_s','axial_w','axial_h',\n", + " 'coronal_s','coronal_w','coronal_h',\n", + " 'sagittal_s','sagittal_w','sagittal_h',\n", + " ]\n", + " data_dict = {}\n", + " for i,k in enumerate(columns): data_dict[k] = [row[i] for row in data]\n", + " return pd.DataFrame(data_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "dimdf = collect_stack_dims(train)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>axial_s</th>\n", + " <th>axial_w</th>\n", + " <th>axial_h</th>\n", + " <th>coronal_s</th>\n", + " <th>coronal_w</th>\n", + " <th>coronal_h</th>\n", + " <th>sagittal_s</th>\n", + " <th>sagittal_w</th>\n", + " <th>sagittal_h</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>50.000000</td>\n", + " <td>50.0</td>\n", + " <td>50.0</td>\n", + " <td>50.000000</td>\n", + " <td>50.0</td>\n", + " <td>50.0</td>\n", + " <td>50.00000</td>\n", + " <td>50.0</td>\n", + " <td>50.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>35.860000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>31.360000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>31.72000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>7.050865</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>7.899264</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>6.35687</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>22.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>18.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>19.00000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>32.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>24.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>26.25000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>37.500000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>32.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>32.00000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>40.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>37.750000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>36.00000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>51.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>46.000000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " <td>46.00000</td>\n", + " <td>256.0</td>\n", + " <td>256.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " axial_s axial_w axial_h coronal_s coronal_w coronal_h \\\n", + "count 50.000000 50.0 50.0 50.000000 50.0 50.0 \n", + "mean 35.860000 256.0 256.0 31.360000 256.0 256.0 \n", + "std 7.050865 0.0 0.0 7.899264 0.0 0.0 \n", + "min 22.000000 256.0 256.0 18.000000 256.0 256.0 \n", + "25% 32.000000 256.0 256.0 24.000000 256.0 256.0 \n", + "50% 37.500000 256.0 256.0 32.000000 256.0 256.0 \n", + "75% 40.000000 256.0 256.0 37.750000 256.0 256.0 \n", + "max 51.000000 256.0 256.0 46.000000 256.0 256.0 \n", + "\n", + " sagittal_s sagittal_w sagittal_h \n", + "count 50.00000 50.0 50.0 \n", + "mean 31.72000 256.0 256.0 \n", + "std 6.35687 0.0 0.0 \n", + "min 19.00000 256.0 256.0 \n", + "25% 26.25000 256.0 256.0 \n", + "50% 32.00000 256.0 256.0 \n", + "75% 36.00000 256.0 256.0 \n", + "max 46.00000 256.0 256.0 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dimdf.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of images in a set varies from case (patient) to case, and the dimensions of each image is the same, 256x256. In the sample of data collected here, axial sequences range in length from 22 to 51; coronal, from 18 to 46; sagittal, from 19 to 46." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def load_one_stack(case, data_path=train_path, plane='coronal'):\n", + " fpath = data_path/plane/'{}.npy'.format(case)\n", + " return np.load(fpath)\n", + "\n", + "def load_stacks(case):\n", + " x = {}\n", + " planes = ['axial', 'coronal', 'sagittal']\n", + " for i, plane in enumerate(planes):\n", + " x[plane] = load_one_stack(case, plane=plane)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(36, 256, 256)\n", + "255\n" + ] + } + ], + "source": [ + "case = train_abnl.Case[0]\n", + "x = load_one_stack(case, plane='coronal')\n", + "print(x.shape)\n", + "print(x.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'axial': array([[[ 0, 0, 0, 0, ..., 4, 5, 4, 3],\n", + " [ 0, 0, 0, 0, ..., 8, 8, 6, 8],\n", + " [ 0, 0, 0, 0, ..., 14, 14, 11, 11],\n", + " [ 0, 0, 0, 0, ..., 16, 16, 14, 15],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 14, 15, 18, 16],\n", + " [ 0, 0, 0, 0, ..., 15, 16, 15, 12],\n", + " [ 0, 0, 0, 0, ..., 11, 12, 13, 12],\n", + " [ 0, 0, 0, 0, ..., 8, 11, 7, 9]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 4, 3, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 5, 9, 7, 7],\n", + " [ 0, 0, 0, 0, ..., 10, 13, 10, 10],\n", + " [ 0, 0, 0, 0, ..., 14, 14, 19, 17],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 18, 16, 16, 17],\n", + " [ 0, 0, 0, 0, ..., 13, 12, 15, 13],\n", + " [ 0, 0, 0, 0, ..., 16, 14, 12, 12],\n", + " [ 0, 0, 0, 0, ..., 8, 6, 5, 7]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 7, 8, 6, 6],\n", + " [ 0, 0, 0, 0, ..., 12, 11, 13, 10],\n", + " [ 0, 0, 0, 0, ..., 12, 18, 18, 16],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 16, 18, 16, 17],\n", + " [ 0, 0, 0, 0, ..., 15, 13, 13, 16],\n", + " [ 0, 0, 0, 0, ..., 10, 10, 10, 12],\n", + " [ 0, 0, 0, 0, ..., 6, 6, 6, 5]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 5, 7, 5, 4],\n", + " [ 0, 0, 0, 0, ..., 11, 10, 11, 12],\n", + " [ 0, 0, 0, 0, ..., 16, 16, 15, 14],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 17, 21, 20, 18],\n", + " [ 0, 0, 0, 0, ..., 14, 15, 18, 14],\n", + " [ 0, 0, 0, 0, ..., 11, 9, 8, 10],\n", + " [ 0, 0, 0, 0, ..., 5, 5, 5, 5]],\n", + " \n", + " ...,\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 4, 4, 4, 4],\n", + " [ 0, 0, 0, 0, ..., 10, 9, 9, 10],\n", + " [ 0, 0, 0, 0, ..., 12, 13, 16, 14],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 16, 12, 14, 15],\n", + " [ 0, 0, 0, 0, ..., 11, 10, 13, 10],\n", + " [ 0, 0, 0, 0, ..., 9, 12, 9, 9],\n", + " [ 0, 0, 0, 0, ..., 6, 6, 4, 5]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n", + " [ 0, 0, 0, 0, ..., 12, 8, 9, 11],\n", + " [ 0, 0, 0, 0, ..., 16, 17, 12, 13],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 13, 14, 14, 17],\n", + " [ 0, 0, 0, 0, ..., 12, 14, 16, 13],\n", + " [ 0, 0, 0, 0, ..., 9, 12, 12, 8],\n", + " [ 0, 0, 0, 0, ..., 6, 5, 4, 5]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 3, 2, 3, 2],\n", + " [ 0, 0, 0, 0, ..., 5, 4, 5, 6],\n", + " [ 0, 0, 0, 0, ..., 13, 11, 11, 9],\n", + " [ 0, 0, 0, 0, ..., 13, 13, 16, 16],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 17, 17, 14, 16],\n", + " [ 0, 0, 0, 0, ..., 16, 14, 16, 15],\n", + " [ 0, 0, 0, 0, ..., 9, 8, 10, 8],\n", + " [ 0, 0, 0, 0, ..., 6, 6, 6, 6]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 5, 4, 4, 4],\n", + " [ 0, 0, 0, 0, ..., 9, 8, 7, 6],\n", + " [ 0, 0, 0, 0, ..., 11, 9, 10, 11],\n", + " [ 0, 0, 0, 0, ..., 17, 15, 12, 13],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 12, 16, 16, 15],\n", + " [ 0, 0, 0, 0, ..., 16, 12, 15, 16],\n", + " [ 0, 0, 0, 0, ..., 10, 14, 12, 12],\n", + " [ 0, 0, 0, 0, ..., 6, 8, 7, 7]]], dtype=uint8),\n", + " 'coronal': array([[[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 1, 2],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 1, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 1, 1, 0],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 3, 3, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 0, 1, 1, 1]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 1, 0, 1],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n", + " \n", + " ...,\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n", + " [ 0, 0, 0, 0, ..., 5, 4, 4, 5],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 5, 5, 4, 5],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 1, 0, 0, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 4, 5, 4, 4],\n", + " [ 0, 0, 0, 0, ..., 7, 8, 8, 7],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 7, 7, 6, 6],\n", + " [ 0, 0, 0, 0, ..., 5, 3, 3, 5],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 2, 3, 3, 3],\n", + " [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n", + " [ 0, 0, 0, 0, ..., 9, 6, 8, 10],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 8, 9, 8, 11],\n", + " [ 0, 0, 0, 0, ..., 5, 6, 3, 4],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 1, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 5, 5],\n", + " [ 0, 0, 0, 0, ..., 9, 10, 7, 6],\n", + " ...,\n", + " [ 0, 0, 0, 0, ..., 9, 9, 9, 8],\n", + " [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8),\n", + " 'sagittal': array([[[ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 8, 8, 8, 7],\n", + " [ 0, 0, 0, 0, ..., 11, 15, 16, 14],\n", + " [ 7, 5, 5, 7, ..., 15, 14, 19, 16],\n", + " ...,\n", + " [ 7, 5, 7, 5, ..., 15, 17, 15, 14],\n", + " [ 0, 1, 1, 1, ..., 10, 15, 12, 11],\n", + " [ 0, 0, 0, 0, ..., 7, 7, 6, 5],\n", + " [ 0, 0, 0, 0, ..., 3, 2, 2, 1]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 4, 3, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 10, 9, 7, 8],\n", + " [ 0, 0, 0, 0, ..., 17, 17, 15, 17],\n", + " [ 7, 5, 5, 6, ..., 20, 22, 21, 17],\n", + " ...,\n", + " [ 6, 7, 6, 4, ..., 18, 14, 19, 19],\n", + " [ 0, 1, 0, 2, ..., 16, 13, 15, 16],\n", + " [ 0, 0, 0, 0, ..., 10, 8, 5, 6],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 1, 4]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 3, 4, 3, 4],\n", + " [ 0, 0, 0, 0, ..., 9, 12, 9, 10],\n", + " [ 0, 0, 0, 0, ..., 19, 20, 12, 11],\n", + " [ 7, 6, 7, 6, ..., 19, 17, 13, 16],\n", + " ...,\n", + " [ 3, 6, 6, 4, ..., 27, 27, 14, 19],\n", + " [ 2, 3, 1, 1, ..., 23, 19, 14, 15],\n", + " [ 0, 0, 0, 0, ..., 9, 4, 11, 9],\n", + " [ 0, 0, 0, 0, ..., 2, 1, 2, 3]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 3, 6, 4, 3],\n", + " [ 0, 0, 0, 0, ..., 8, 10, 8, 10],\n", + " [ 0, 0, 0, 0, ..., 21, 18, 14, 18],\n", + " [ 5, 9, 6, 6, ..., 26, 19, 18, 22],\n", + " ...,\n", + " [ 6, 4, 6, 9, ..., 22, 23, 24, 25],\n", + " [ 1, 1, 2, 1, ..., 14, 17, 19, 17],\n", + " [ 0, 0, 0, 0, ..., 10, 8, 7, 9],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 2, 3]],\n", + " \n", + " ...,\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n", + " [ 0, 0, 0, 0, ..., 10, 8, 10, 10],\n", + " ...,\n", + " [ 0, 0, 0, 3, ..., 7, 10, 8, 7],\n", + " [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n", + " [ 0, 0, 0, 0, ..., 2, 2, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n", + " [ 0, 0, 0, 0, ..., 10, 11, 9, 11],\n", + " ...,\n", + " [ 0, 0, 0, 4, ..., 10, 9, 10, 9],\n", + " [ 0, 0, 0, 1, ..., 5, 6, 5, 6],\n", + " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 3, 2, 2, 2],\n", + " [ 0, 0, 0, 1, ..., 11, 12, 11, 9],\n", + " ...,\n", + " [ 0, 0, 0, 3, ..., 9, 11, 10, 9],\n", + " [ 0, 0, 0, 1, ..., 5, 6, 6, 5],\n", + " [ 0, 0, 0, 0, ..., 2, 3, 2, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", + " \n", + " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 8, 10, 8, 9],\n", + " ...,\n", + " [ 0, 0, 0, 2, ..., 7, 9, 8, 8],\n", + " [ 0, 0, 0, 0, ..., 4, 4, 4, 5],\n", + " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", + " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8)}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_multi = load_stacks(case)\n", + "x_multi" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from ipywidgets import interactive\n", + "from IPython.display import display\n", + "\n", + "plt.style.use('grayscale')\n", + "\n", + "class KneePlot():\n", + " def __init__(self, x, figsize=(10, 10)):\n", + " self.x = x\n", + " self.slice_range = (0, self.x.shape[0] - 1)\n", + " self.resize(figsize)\n", + " \n", + " def _plot_slice(self, im_slice):\n", + " fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n", + " ax.imshow(self.x[im_slice, :, :])\n", + " plt.show()\n", + "\n", + " def resize(self, figsize):\n", + " self.figsize = figsize\n", + " self.interactive_plot = interactive(self._plot_slice, im_slice=self.slice_range)\n", + " self.output = self.interactive_plot.children[-1]\n", + " self.output.layout.height = '{}px'.format(60 * self.figsize[1])\n", + "\n", + " def show(self):\n", + " display(self.interactive_plot)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bb94b9e5a31b44c5abd287a8cdb12fe9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='600px'…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot = KneePlot(x)\n", + "plot.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4d55829c45294dbd90f82665f799a8ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='720px'…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot.resize(figsize=(12, 12))\n", + "plot.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from ipywidgets import interact, Dropdown, IntSlider\n", + "\n", + "class MultiKneePlot():\n", + " def __init__(self, x_multi, figsize=(10, 10)):\n", + " self.x = x_multi\n", + " self.planes = ['coronal', 'sagittal', 'axial']\n", + " self.slice_nums = {plane: self.x[plane].shape[0] for plane in self.planes}\n", + " self.figsize = figsize\n", + " \n", + " def _plot_slices(self, plane, im_slice): \n", + " fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n", + " ax.imshow(self.x[plane][im_slice, :, :])\n", + " plt.show()\n", + " \n", + " def draw(self):\n", + " planes_widget = Dropdown(options=self.planes)\n", + " plane_init = self.planes[0]\n", + " slice_init = self.slice_nums[plane_init] - 1\n", + " slices_widget = IntSlider(min=0, max=slice_init, value=slice_init//2)\n", + " def update_slices_widget(*args):\n", + " slices_widget.max = self.slice_nums[planes_widget.value] - 1\n", + " slices_widget.value = slices_widget.max // 2\n", + " planes_widget.observe(update_slices_widget, 'value')\n", + " interact(self._plot_slices, plane=planes_widget, im_slice=slices_widget)\n", + " \n", + " def resize(self, figsize): self.figsize = figsize\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0d7a1c151c3440c59bfa0a2b55e6180e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='plane', options=('coronal', 'sagittal', 'axial'), value='coronal')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_multi = MultiKneePlot(x_multi)\n", + "plot_multi.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}