{ "cells": [ { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pydicom\n", "import matplotlib.image as mpimg\n", "from tqdm import tqdm_notebook\n", "import cv2\n", "import os\n", "import re\n", "from scipy import ndimage\n", "from skimage import morphology\n", "import PIL" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "base_url = '/home/ubuntu/kaggle/rsna-intracranial-hemorrhage-detection/'" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "TRAIN_DIR = '/home/ubuntu/kaggle/rsna-intracranial-hemorrhage-detection/stage_2_train'\n", "TEST_DIR = '/home/ubuntu/kaggle/rsna-intracranial-hemorrhage-detection/stage_2_test'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "752803\r\n" ] } ], "source": [ "! ls {TRAIN_DIR} | wc -l" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "121232\r\n" ] } ], "source": [ "! ls {TEST_DIR} | wc -l" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID_000012eaf.dcm\n", "ID_000039fa0.dcm\n", "ID_00005679d.dcm\n", "ID_00008ce3c.dcm\n", "ID_0000950d7.dcm\n", "ls: write error: Broken pipe\n" ] } ], "source": [ "! ls {TRAIN_DIR} | head -n 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the labels & metadata\n", "The metadata was extracted beforehand using pydicom. This takes a while so I saved the results in these parquet files so they don't need to be generated each time." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Diagnosisanyepiduralintraparenchymalintraventricularsubarachnoidsubdural
ImageID
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" ], "text/plain": [ "Diagnosis any epidural intraparenchymal intraventricular subarachnoid \\\n", "ImageID \n", "ID_000012eaf 0 0 0 0 0 \n", "ID_000039fa0 0 0 0 0 0 \n", "ID_00005679d 0 0 0 0 0 \n", "ID_00008ce3c 0 0 0 0 0 \n", "ID_0000950d7 0 0 0 0 0 \n", "\n", "Diagnosis subdural \n", "ImageID \n", "ID_000012eaf 0 \n", "ID_000039fa0 0 \n", "ID_00005679d 0 \n", "ID_00008ce3c 0 \n", "ID_0000950d7 0 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = pd.read_csv(f'{base_url}/stage_2_train.csv').drop_duplicates()\n", "train_df['ImageID'] = train_df['ID'].str.slice(stop=12)\n", "train_df['Diagnosis'] = train_df['ID'].str.slice(start=13)\n", "train_labels = train_df.pivot(index=\"ImageID\", columns=\"Diagnosis\", values=\"Label\")\n", "train_labels.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def get_metadata(image_dir):\n", "\n", " labels = [\n", " 'BitsAllocated', 'BitsStored', 'Columns', 'HighBit', \n", " 'ImageOrientationPatient_0', 'ImageOrientationPatient_1', 'ImageOrientationPatient_2',\n", " 'ImageOrientationPatient_3', 'ImageOrientationPatient_4', 'ImageOrientationPatient_5',\n", " 'ImagePositionPatient_0', 'ImagePositionPatient_1', 'ImagePositionPatient_2',\n", " 'Modality', 'PatientID', 'PhotometricInterpretation', 'PixelRepresentation',\n", " 'PixelSpacing_0', 'PixelSpacing_1', 'RescaleIntercept', 'RescaleSlope', 'Rows', 'SOPInstanceUID',\n", " 'SamplesPerPixel', 'SeriesInstanceUID', 'StudyID', 'StudyInstanceUID', \n", " 'WindowCenter', 'WindowWidth', 'Image',\n", " ]\n", "\n", " data = {l: [] for l in labels}\n", "\n", " for image in tqdm_notebook(os.listdir(image_dir)):\n", " data[\"Image\"].append(image[:-4])\n", "\n", " ds = pydicom.dcmread(os.path.join(image_dir, image))\n", "\n", " for metadata in ds.dir():\n", " if metadata != \"PixelData\":\n", " metadata_values = getattr(ds, metadata)\n", " if type(metadata_values) == pydicom.multival.MultiValue and metadata not in [\"WindowCenter\", \"WindowWidth\"]:\n", " for i, v in enumerate(metadata_values):\n", " data[f\"{metadata}_{i}\"].append(v)\n", " else:\n", " if type(metadata_values) == pydicom.multival.MultiValue and metadata in [\"WindowCenter\", \"WindowWidth\"]:\n", " data[metadata].append(metadata_values[0])\n", " else:\n", " data[metadata].append(metadata_values)\n", "\n", " return pd.DataFrame(data).set_index(\"Image\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Generate metadata dataframes\n", "train_metadata = get_metadata(os.path.join(base_url, \"stage_2_train\"))\n", "test_metadata = get_metadata(os.path.join(base_url, \"stage_2_test\"))\n", "\n", "train_metadata.to_parquet(f'{base_url}/train_metadata.parquet.gzip', compression='gzip')\n", "test_metadata.to_parquet(f'{base_url}/test_metadata.parquet.gzip', compression='gzip')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ImageID_d45673798ID_74cfe18f9ID_d7e2f42eeID_e6c5352eaID_688b50fa0ID_ef18adb45ID_94544b40dID_2a019f628ID_9bffe2b90ID_7ccdde5eb...ID_72b376d48ID_bf64dc996ID_7355aedc3ID_89ce8ad00ID_3a25fd051ID_529052515ID_f540aa7fbID_5f0c548d7ID_2bc16380cID_c161feeeb
BitsAllocated16161616161616161616...16161616161616161616
BitsStored16161212161216161216...16161212121616161216
Columns512512512512512512512512512512...512512512512512512512512512512
HighBit15151111151115151115...15151111111515151115
ImageOrientationPatient_01111111111...1111111111
ImageOrientationPatient_10000000000...0000000000
ImageOrientationPatient_20000000000...0000000000
ImageOrientationPatient_30000000000...0000000000
ImageOrientationPatient_40.9455191110.99357210.9271840.9063080.9816271...10.9271841110.9271840.9925460.99254610.95882
ImageOrientationPatient_5-0.325568000-0.1132030-0.374607-0.422618-0.1908090...0-0.374607000-0.374607-0.121869-0.1218690-0.284015
ImagePositionPatient_0-125-126.409-117-120-125-125-125-125-125-125...-126.409-125-125-113-114.5-125-125-125-125-125
ImagePositionPatient_1-123.39-126.409-34-114.296-18-95.198-114.88825.315-88.753...-126.409-107.598-75-1.5-64.598-119.368-124.568-8-119.852
ImagePositionPatient_272.53372.5-123.5151.368.7595123.821.317843.925190.972-6.642...17.5157.373154.7148.8726.3119.517108.0624.56384127.9140.066
ModalityCTCTCTCTCTCTCTCTCTCT...CTCTCTCTCTCTCTCTCTCT
PatientIDID_815113f2ID_2cec0d8aID_85f4970cID_7dcb798fID_37d50406ID_7c193c9eID_520fd258ID_9481b02bID_d96101faID_2f572f12...ID_96f03221ID_9e730feeID_d55f931cID_c1ee6b75ID_e9fe4085ID_3c45cca3ID_1a9dfcf1ID_f52de020ID_cb69cfb2ID_ec0c2ac1
PhotometricInterpretationMONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2...MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2MONOCHROME2
PixelRepresentation1100101101...1100011101
PixelSpacing_00.4882810.4947510.4882810.468750.4882810.4882810.4882810.4882810.4882810.488281...0.4947510.4882810.4882810.4414060.4472660.4882810.4882810.4882810.4882810.488281
PixelSpacing_10.4882810.4947510.4882810.468750.4882810.4882810.4882810.4882810.4882810.488281...0.4947510.4882810.4882810.4414060.4472660.4882810.4882810.4882810.4882810.488281
RescaleIntercept-1024-1024-1024-1024-1024-1024-1024-1024-1024-1024...-1024-1024-1024-1024-1024-1024-1024-1024-1024-1024
RescaleSlope1111111111...1111111111
Rows512512512512512512512512512512...512512512512512512512512512512
SOPInstanceUIDID_d45673798ID_74cfe18f9ID_d7e2f42eeID_e6c5352eaID_688b50fa0ID_ef18adb45ID_94544b40dID_2a019f628ID_9bffe2b90ID_7ccdde5eb...ID_72b376d48ID_bf64dc996ID_7355aedc3ID_89ce8ad00ID_3a25fd051ID_529052515ID_f540aa7fbID_5f0c548d7ID_2bc16380cID_c161feeeb
SamplesPerPixel1111111111...1111111111
SeriesInstanceUIDID_8149e17d30ID_6efe302471ID_3839240e5dID_2fe1c5668fID_c150ebbdbcID_22d6dfed97ID_f69325ded8ID_d56043f46dID_e21f790abbID_874ba6cb8c...ID_cae12af5f5ID_45066b940fID_db90430b69ID_983b1918c0ID_79b3575178ID_443e596744ID_78a079be31ID_9ae9e18e3fID_cd5661831bID_bb8d7f36b6
StudyID...
StudyInstanceUIDID_341bf815cdID_8e108d767cID_95184e7272ID_0b499345faID_4be47d5353ID_10f12f8722ID_66a7835a03ID_4fb3cbd983ID_74e913165eID_4598fe5069...ID_51270c92ceID_1b4156b6a4ID_12f15437fdID_b2e6ee660aID_ea15b88236ID_7b72f23129ID_acc44cec79ID_f41f22feddID_f986b69079ID_384ec0d10a
WindowCenter30353636303630304050...35303636363030303630
WindowWidth8013580808080808080100...135808080808080808080
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29 rows × 752803 columns

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" ], "text/plain": [ "Image ID_d45673798 ID_74cfe18f9 ID_d7e2f42ee \\\n", "BitsAllocated 16 16 16 \n", "BitsStored 16 16 12 \n", "Columns 512 512 512 \n", "HighBit 15 15 11 \n", "ImageOrientationPatient_0 1 1 1 \n", "ImageOrientationPatient_1 0 0 0 \n", "ImageOrientationPatient_2 0 0 0 \n", "ImageOrientationPatient_3 0 0 0 \n", "ImageOrientationPatient_4 0.945519 1 1 \n", "ImageOrientationPatient_5 -0.325568 0 0 \n", "ImagePositionPatient_0 -125 -126.409 -117 \n", "ImagePositionPatient_1 -123.39 -126.409 -3 \n", "ImagePositionPatient_2 72.533 72.5 -123.5 \n", "Modality CT CT CT \n", "PatientID ID_815113f2 ID_2cec0d8a ID_85f4970c \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 1 1 0 \n", "PixelSpacing_0 0.488281 0.494751 0.488281 \n", "PixelSpacing_1 0.488281 0.494751 0.488281 \n", "RescaleIntercept -1024 -1024 -1024 \n", "RescaleSlope 1 1 1 \n", "Rows 512 512 512 \n", "SOPInstanceUID ID_d45673798 ID_74cfe18f9 ID_d7e2f42ee \n", "SamplesPerPixel 1 1 1 \n", "SeriesInstanceUID ID_8149e17d30 ID_6efe302471 ID_3839240e5d \n", "StudyID \n", "StudyInstanceUID ID_341bf815cd ID_8e108d767c ID_95184e7272 \n", "WindowCenter 30 35 36 \n", "WindowWidth 80 135 80 \n", "\n", "Image ID_e6c5352ea ID_688b50fa0 ID_ef18adb45 \\\n", "BitsAllocated 16 16 16 \n", "BitsStored 12 16 12 \n", "Columns 512 512 512 \n", "HighBit 11 15 11 \n", "ImageOrientationPatient_0 1 1 1 \n", "ImageOrientationPatient_1 0 0 0 \n", "ImageOrientationPatient_2 0 0 0 \n", "ImageOrientationPatient_3 0 0 0 \n", "ImageOrientationPatient_4 1 0.993572 1 \n", "ImageOrientationPatient_5 0 -0.113203 0 \n", "ImagePositionPatient_0 -120 -125 -125 \n", "ImagePositionPatient_1 4 -114.296 -18 \n", "ImagePositionPatient_2 151.3 68.7595 123.8 \n", "Modality CT CT CT \n", "PatientID ID_7dcb798f ID_37d50406 ID_7c193c9e \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 0 1 0 \n", "PixelSpacing_0 0.46875 0.488281 0.488281 \n", "PixelSpacing_1 0.46875 0.488281 0.488281 \n", "RescaleIntercept -1024 -1024 -1024 \n", "RescaleSlope 1 1 1 \n", "Rows 512 512 512 \n", "SOPInstanceUID ID_e6c5352ea ID_688b50fa0 ID_ef18adb45 \n", "SamplesPerPixel 1 1 1 \n", "SeriesInstanceUID ID_2fe1c5668f ID_c150ebbdbc ID_22d6dfed97 \n", "StudyID \n", "StudyInstanceUID ID_0b499345fa ID_4be47d5353 ID_10f12f8722 \n", "WindowCenter 36 30 36 \n", "WindowWidth 80 80 80 \n", "\n", "Image ID_94544b40d ID_2a019f628 ID_9bffe2b90 \\\n", "BitsAllocated 16 16 16 \n", "BitsStored 16 16 12 \n", "Columns 512 512 512 \n", "HighBit 15 15 11 \n", "ImageOrientationPatient_0 1 1 1 \n", "ImageOrientationPatient_1 0 0 0 \n", "ImageOrientationPatient_2 0 0 0 \n", "ImageOrientationPatient_3 0 0 0 \n", "ImageOrientationPatient_4 0.927184 0.906308 0.981627 \n", "ImageOrientationPatient_5 -0.374607 -0.422618 -0.190809 \n", "ImagePositionPatient_0 -125 -125 -125 \n", "ImagePositionPatient_1 -95.198 -114.888 25.315 \n", "ImagePositionPatient_2 21.3178 43.925 190.972 \n", "Modality CT CT CT \n", "PatientID ID_520fd258 ID_9481b02b ID_d96101fa \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 1 1 0 \n", "PixelSpacing_0 0.488281 0.488281 0.488281 \n", "PixelSpacing_1 0.488281 0.488281 0.488281 \n", "RescaleIntercept -1024 -1024 -1024 \n", "RescaleSlope 1 1 1 \n", "Rows 512 512 512 \n", "SOPInstanceUID ID_94544b40d ID_2a019f628 ID_9bffe2b90 \n", "SamplesPerPixel 1 1 1 \n", "SeriesInstanceUID ID_f69325ded8 ID_d56043f46d ID_e21f790abb \n", "StudyID \n", "StudyInstanceUID ID_66a7835a03 ID_4fb3cbd983 ID_74e913165e \n", "WindowCenter 30 30 40 \n", "WindowWidth 80 80 80 \n", "\n", "Image ID_7ccdde5eb ... ID_72b376d48 ID_bf64dc996 \\\n", "BitsAllocated 16 ... 16 16 \n", "BitsStored 16 ... 16 16 \n", "Columns 512 ... 512 512 \n", "HighBit 15 ... 15 15 \n", "ImageOrientationPatient_0 1 ... 1 1 \n", "ImageOrientationPatient_1 0 ... 0 0 \n", "ImageOrientationPatient_2 0 ... 0 0 \n", "ImageOrientationPatient_3 0 ... 0 0 \n", "ImageOrientationPatient_4 1 ... 1 0.927184 \n", "ImageOrientationPatient_5 0 ... 0 -0.374607 \n", "ImagePositionPatient_0 -125 ... -126.409 -125 \n", "ImagePositionPatient_1 -88.753 ... -126.409 -107.598 \n", "ImagePositionPatient_2 -6.642 ... 17.5 157.373 \n", "Modality CT ... CT CT \n", "PatientID ID_2f572f12 ... ID_96f03221 ID_9e730fee \n", "PhotometricInterpretation MONOCHROME2 ... MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 1 ... 1 1 \n", "PixelSpacing_0 0.488281 ... 0.494751 0.488281 \n", "PixelSpacing_1 0.488281 ... 0.494751 0.488281 \n", "RescaleIntercept -1024 ... -1024 -1024 \n", "RescaleSlope 1 ... 1 1 \n", "Rows 512 ... 512 512 \n", "SOPInstanceUID ID_7ccdde5eb ... ID_72b376d48 ID_bf64dc996 \n", "SamplesPerPixel 1 ... 1 1 \n", "SeriesInstanceUID ID_874ba6cb8c ... ID_cae12af5f5 ID_45066b940f \n", "StudyID ... \n", "StudyInstanceUID ID_4598fe5069 ... ID_51270c92ce ID_1b4156b6a4 \n", "WindowCenter 50 ... 35 30 \n", "WindowWidth 100 ... 135 80 \n", "\n", "Image ID_7355aedc3 ID_89ce8ad00 ID_3a25fd051 \\\n", "BitsAllocated 16 16 16 \n", "BitsStored 12 12 12 \n", "Columns 512 512 512 \n", "HighBit 11 11 11 \n", "ImageOrientationPatient_0 1 1 1 \n", "ImageOrientationPatient_1 0 0 0 \n", "ImageOrientationPatient_2 0 0 0 \n", "ImageOrientationPatient_3 0 0 0 \n", "ImageOrientationPatient_4 1 1 1 \n", "ImageOrientationPatient_5 0 0 0 \n", "ImagePositionPatient_0 -125 -113 -114.5 \n", "ImagePositionPatient_1 -7 5 -1.5 \n", "ImagePositionPatient_2 154.7 148.8 726.3 \n", "Modality CT CT CT \n", "PatientID ID_d55f931c ID_c1ee6b75 ID_e9fe4085 \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 0 0 0 \n", "PixelSpacing_0 0.488281 0.441406 0.447266 \n", "PixelSpacing_1 0.488281 0.441406 0.447266 \n", "RescaleIntercept -1024 -1024 -1024 \n", "RescaleSlope 1 1 1 \n", "Rows 512 512 512 \n", "SOPInstanceUID ID_7355aedc3 ID_89ce8ad00 ID_3a25fd051 \n", "SamplesPerPixel 1 1 1 \n", "SeriesInstanceUID ID_db90430b69 ID_983b1918c0 ID_79b3575178 \n", "StudyID \n", "StudyInstanceUID ID_12f15437fd ID_b2e6ee660a ID_ea15b88236 \n", "WindowCenter 36 36 36 \n", "WindowWidth 80 80 80 \n", "\n", "Image ID_529052515 ID_f540aa7fb ID_5f0c548d7 \\\n", "BitsAllocated 16 16 16 \n", "BitsStored 16 16 16 \n", "Columns 512 512 512 \n", "HighBit 15 15 15 \n", "ImageOrientationPatient_0 1 1 1 \n", "ImageOrientationPatient_1 0 0 0 \n", "ImageOrientationPatient_2 0 0 0 \n", "ImageOrientationPatient_3 0 0 0 \n", "ImageOrientationPatient_4 0.927184 0.992546 0.992546 \n", "ImageOrientationPatient_5 -0.374607 -0.121869 -0.121869 \n", "ImagePositionPatient_0 -125 -125 -125 \n", "ImagePositionPatient_1 -64.598 -119.368 -124.568 \n", "ImagePositionPatient_2 119.517 108.062 4.56384 \n", "Modality CT CT CT \n", "PatientID ID_3c45cca3 ID_1a9dfcf1 ID_f52de020 \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 1 1 1 \n", "PixelSpacing_0 0.488281 0.488281 0.488281 \n", "PixelSpacing_1 0.488281 0.488281 0.488281 \n", "RescaleIntercept -1024 -1024 -1024 \n", "RescaleSlope 1 1 1 \n", "Rows 512 512 512 \n", "SOPInstanceUID ID_529052515 ID_f540aa7fb ID_5f0c548d7 \n", "SamplesPerPixel 1 1 1 \n", "SeriesInstanceUID ID_443e596744 ID_78a079be31 ID_9ae9e18e3f \n", "StudyID \n", "StudyInstanceUID ID_7b72f23129 ID_acc44cec79 ID_f41f22fedd \n", "WindowCenter 30 30 30 \n", "WindowWidth 80 80 80 \n", "\n", "Image ID_2bc16380c ID_c161feeeb \n", "BitsAllocated 16 16 \n", "BitsStored 12 16 \n", "Columns 512 512 \n", "HighBit 11 15 \n", "ImageOrientationPatient_0 1 1 \n", "ImageOrientationPatient_1 0 0 \n", "ImageOrientationPatient_2 0 0 \n", "ImageOrientationPatient_3 0 0 \n", "ImageOrientationPatient_4 1 0.95882 \n", "ImageOrientationPatient_5 0 -0.284015 \n", "ImagePositionPatient_0 -125 -125 \n", "ImagePositionPatient_1 -8 -119.852 \n", "ImagePositionPatient_2 127.9 140.066 \n", "Modality CT CT \n", "PatientID ID_cb69cfb2 ID_ec0c2ac1 \n", "PhotometricInterpretation MONOCHROME2 MONOCHROME2 \n", "PixelRepresentation 0 1 \n", "PixelSpacing_0 0.488281 0.488281 \n", "PixelSpacing_1 0.488281 0.488281 \n", "RescaleIntercept -1024 -1024 \n", "RescaleSlope 1 1 \n", "Rows 512 512 \n", "SOPInstanceUID ID_2bc16380c ID_c161feeeb \n", "SamplesPerPixel 1 1 \n", "SeriesInstanceUID ID_cd5661831b ID_bb8d7f36b6 \n", "StudyID \n", "StudyInstanceUID ID_f986b69079 ID_384ec0d10a \n", "WindowCenter 36 30 \n", "WindowWidth 80 80 \n", "\n", "[29 rows x 752803 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_metadata.T" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BitsAllocatedBitsStoredColumnsDatasetHighBitImageOrientationPatient_0ImageOrientationPatient_1ImageOrientationPatient_2ImageOrientationPatient_3ImageOrientationPatient_4...StudyIDStudyInstanceUIDWindowCenterWindowWidthanyepiduralintraparenchymalintraventricularsubarachnoidsubdural
Image
ID_24250ffbc1612512train111.00.00.00.00.920505...ID_6222a3935b40.080.00.00.00.00.00.00.0
ID_6e8c8d6501612512train111.00.00.00.00.920505...ID_6222a3935b40.080.00.00.00.00.00.00.0
ID_ac042708d1612512train111.00.00.00.00.920505...ID_6222a3935b40.080.00.00.00.00.00.00.0
ID_d1e2a17a91612512train111.00.00.00.00.927184...ID_a5fb90389840.080.00.00.00.00.00.00.0
ID_e1a1b45a51612512train111.00.00.00.00.920505...ID_6222a3935b40.080.00.00.00.00.00.00.0
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5 rows × 36 columns

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" ], "text/plain": [ " BitsAllocated BitsStored Columns Dataset HighBit \\\n", "Image \n", "ID_24250ffbc 16 12 512 train 11 \n", "ID_6e8c8d650 16 12 512 train 11 \n", "ID_ac042708d 16 12 512 train 11 \n", "ID_d1e2a17a9 16 12 512 train 11 \n", "ID_e1a1b45a5 16 12 512 train 11 \n", "\n", " ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n", "Image \n", "ID_24250ffbc 1.0 0.0 \n", "ID_6e8c8d650 1.0 0.0 \n", "ID_ac042708d 1.0 0.0 \n", "ID_d1e2a17a9 1.0 0.0 \n", "ID_e1a1b45a5 1.0 0.0 \n", "\n", " ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n", "Image \n", "ID_24250ffbc 0.0 0.0 \n", "ID_6e8c8d650 0.0 0.0 \n", "ID_ac042708d 0.0 0.0 \n", "ID_d1e2a17a9 0.0 0.0 \n", "ID_e1a1b45a5 0.0 0.0 \n", "\n", " ImageOrientationPatient_4 ... StudyID StudyInstanceUID \\\n", "Image ... \n", "ID_24250ffbc 0.920505 ... ID_6222a3935b \n", "ID_6e8c8d650 0.920505 ... ID_6222a3935b \n", "ID_ac042708d 0.920505 ... ID_6222a3935b \n", "ID_d1e2a17a9 0.927184 ... ID_a5fb903898 \n", "ID_e1a1b45a5 0.920505 ... ID_6222a3935b \n", "\n", " WindowCenter WindowWidth any epidural intraparenchymal \\\n", "Image \n", "ID_24250ffbc 40.0 80.0 0.0 0.0 0.0 \n", "ID_6e8c8d650 40.0 80.0 0.0 0.0 0.0 \n", "ID_ac042708d 40.0 80.0 0.0 0.0 0.0 \n", "ID_d1e2a17a9 40.0 80.0 0.0 0.0 0.0 \n", "ID_e1a1b45a5 40.0 80.0 0.0 0.0 0.0 \n", "\n", " intraventricular subarachnoid subdural \n", "Image \n", "ID_24250ffbc 0.0 0.0 0.0 \n", "ID_6e8c8d650 0.0 0.0 0.0 \n", "ID_ac042708d 0.0 0.0 0.0 \n", "ID_d1e2a17a9 0.0 0.0 0.0 \n", "ID_e1a1b45a5 0.0 0.0 0.0 \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_metadata = pd.read_parquet(f'{base_url}/train_metadata.parquet.gzip')\n", "test_metadata = pd.read_parquet(f'{base_url}/test_metadata.parquet.gzip')\n", "\n", "train_metadata[\"Dataset\"] = \"train\"\n", "test_metadata[\"Dataset\"] = \"test\"\n", "\n", "train_metadata = train_metadata.join(train_labels)\n", "\n", "metadata = pd.concat([train_metadata, test_metadata], sort=True)\n", "metadata.sort_values(by=\"ImagePositionPatient_2\", inplace=True, ascending=False)\n", "metadata.head()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25262" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata[\"StudyInstanceUID\"].nunique()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25262" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata[\"SeriesInstanceUID\"].nunique()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "22456" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata[\"PatientID\"].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Group the unique studies" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "studies = metadata.groupby(\"StudyInstanceUID\")\n", "studies_list = list(studies)\n", "\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BitsAllocatedBitsStoredColumnsDatasetHighBitImageOrientationPatient_0ImageOrientationPatient_1ImageOrientationPatient_2ImageOrientationPatient_3ImageOrientationPatient_4...StudyIDStudyInstanceUIDWindowCenterWindowWidthanyepiduralintraparenchymalintraventricularsubarachnoidsubdural
Image
ID_8651222131616512train151.00.00.00.00.927184...ID_13eda5126c30.080.00.00.00.00.00.00.0
ID_51a1610011616512train151.00.00.00.00.927184...ID_13eda5126c30.080.00.00.00.00.00.00.0
ID_9f9e9b7051616512train151.00.00.00.00.927184...ID_13eda5126c30.080.00.00.00.00.00.00.0
ID_d985de5b71616512train151.00.00.00.00.927184...ID_13eda5126c30.080.00.00.00.00.00.00.0
ID_2837d7a951616512train151.00.00.00.00.927184...ID_13eda5126c30.080.00.00.00.00.00.00.0
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5 rows × 36 columns

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" ], "text/plain": [ " BitsAllocated BitsStored Columns Dataset HighBit \\\n", "Image \n", "ID_865122213 16 16 512 train 15 \n", "ID_51a161001 16 16 512 train 15 \n", "ID_9f9e9b705 16 16 512 train 15 \n", "ID_d985de5b7 16 16 512 train 15 \n", "ID_2837d7a95 16 16 512 train 15 \n", "\n", " ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n", "Image \n", "ID_865122213 1.0 0.0 \n", "ID_51a161001 1.0 0.0 \n", "ID_9f9e9b705 1.0 0.0 \n", "ID_d985de5b7 1.0 0.0 \n", "ID_2837d7a95 1.0 0.0 \n", "\n", " ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n", "Image \n", "ID_865122213 0.0 0.0 \n", "ID_51a161001 0.0 0.0 \n", "ID_9f9e9b705 0.0 0.0 \n", "ID_d985de5b7 0.0 0.0 \n", "ID_2837d7a95 0.0 0.0 \n", "\n", " ImageOrientationPatient_4 ... StudyID StudyInstanceUID \\\n", "Image ... \n", "ID_865122213 0.927184 ... ID_13eda5126c \n", "ID_51a161001 0.927184 ... ID_13eda5126c \n", "ID_9f9e9b705 0.927184 ... ID_13eda5126c \n", "ID_d985de5b7 0.927184 ... ID_13eda5126c \n", "ID_2837d7a95 0.927184 ... ID_13eda5126c \n", "\n", " WindowCenter WindowWidth any epidural intraparenchymal \\\n", "Image \n", "ID_865122213 30.0 80.0 0.0 0.0 0.0 \n", "ID_51a161001 30.0 80.0 0.0 0.0 0.0 \n", "ID_9f9e9b705 30.0 80.0 0.0 0.0 0.0 \n", "ID_d985de5b7 30.0 80.0 0.0 0.0 0.0 \n", "ID_2837d7a95 30.0 80.0 0.0 0.0 0.0 \n", "\n", " intraventricular subarachnoid subdural \n", "Image \n", "ID_865122213 0.0 0.0 0.0 \n", "ID_51a161001 0.0 0.0 0.0 \n", "ID_9f9e9b705 0.0 0.0 0.0 \n", "ID_d985de5b7 0.0 0.0 0.0 \n", "ID_2837d7a95 0.0 0.0 0.0 \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "study_name, study_df = studies_list[2000]\n", "study_df.head()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 25262.000000\n", "mean 34.598805\n", "std 5.080465\n", "min 20.000000\n", "25% 32.000000\n", "50% 33.000000\n", "75% 37.000000\n", "max 60.000000\n", "dtype: float64" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "studies.size().describe()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(studies.size());" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create a 3D volume for a single study\n", "We'll use the first study in the grouped studies which is comprised of 40 individial axial DICOM images" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "def window_img(dcm, width=None, level=None, norm=True):\n", " pixels = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n", " \n", " # Pad non-square images\n", " if pixels.shape[0] != pixels.shape[1]:\n", " (a,b) = pixels.shape\n", " if a > b:\n", " padding = ((0, 0), ((a-b) // 2, (a-b) // 2))\n", " else:\n", " padding = (((b-a) // 2, (b-a) // 2), (0, 0))\n", " pixels = np.pad(pixels, padding, mode='constant', constant_values=0)\n", " \n", " if not width:\n", " width = dcm.WindowWidth\n", " if type(width) != pydicom.valuerep.DSfloat:\n", " width = width[0]\n", " if not level:\n", " level = dcm.WindowCenter\n", " if type(level) != pydicom.valuerep.DSfloat:\n", " level = level[0]\n", " lower = level - (width / 2)\n", " upper = level + (width / 2)\n", " img = np.clip(pixels, lower, upper)\n", " \n", " if norm:\n", " return (img - lower) / (upper - lower)\n", " else:\n", " return img" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "volume, labels = [], []\n", "for index, row in study_df.iterrows():\n", " if row[\"Dataset\"] == \"train\":\n", " dcm = pydicom.dcmread(os.path.join(base_url, \"stage_2_train\", index+\".dcm\"))\n", " else:\n", " dcm = pydicom.dcmread(os.path.join(base_url, \"stage_2_test\", index+\".dcm\"))\n", " \n", " img = window_img(dcm)\n", " label = row[[\"any\", \"epidural\", \"intraparenchymal\", \"intraventricular\", \"subarachnoid\", \"subdural\"]]\n", " volume.append(img)\n", " labels.append(label)\n", " \n", "volume = np.array(volume)\n", "labels = np.array(labels)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((32, 512, 512), (32, 6))" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "volume.shape, labels.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> The provided DICOM images are axial slices. Let's use our new 3D volume to create sagittal and coronal slices\n", "\n", "> * Red line - axial plane\n", "> * Green line - sagittal plane\n", "> * Blue line - coronal plane" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Axial\n", "plt.figure(figsize=(8, 8))\n", "plt.imshow(volume[20, :, :], cmap=plt.cm.bone)\n", "plt.vlines(300, 0, 512, colors='g')\n", "plt.hlines(300, 0, 512, colors='b');" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Sagittal\n", "plt.figure(figsize=(8, 8))\n", "plt.imshow(volume[:, :, 300], aspect=9, cmap=plt.cm.bone)\n", "plt.vlines(300, 0, 40, colors='b')\n", "plt.hlines(20, 0, 512, colors='r');" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Coronal\n", "plt.figure(figsize=(8, 8))\n", "plt.imshow(volume[:, 300, :], aspect=9, cmap=plt.cm.bone)\n", "plt.vlines(300, 0, 40, colors='g')\n", "plt.hlines(20, 0, 512, colors='r');" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }