{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# RSNA-MICCAI Brain Tumor Radiogenomic Classification (EDA)\n> - Adapted from \"https://www.kaggle.com/code/tommzzhou/extensive-eda\"\n> - Referenced Kernels and Other Links:
\n> [1] https://www.kaggle.com/xhlulu/siim-covid-19-convert-to-jpg-256px
\n> [2] https://www.kaggle.com/smoschou55/advanced-eda-brain-tumor-data/comments#Main-Competition-Workflow
\n> [3] https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling
\n> [4] https://www.kaggle.com/ayuraj/brain-tumor-eda-and-interactive-viz-with-w-b
\n> [5] https://case.edu/med/neurology/NR/MRI%20Basics.htm
\n> [6] https://www.kaggle.com/arnabs007/part-1-rsna-miccai-btrc-understanding-the-data
\n> [7] https://www.kaggle.com/sreevishnudamodaran/rsna-3d-clahe-voxels-tpu-3d-augmentations
","metadata":{}},{"cell_type":"markdown","source":"# Sections\n> ### 1. Make sense of provided data & files\n> ### 2. Check for corrupted/weird data\n> ### 3. Visualize data of positive / negative cases\n> ### 4. Explore the data in 3D\n> ### 5. Explore ways to align images / center-crop / other augmentation\n> ### 6. My opinion on the ultimate goal of this competition","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nimport os\nfrom tensorflow import keras\nimport sys\nimport random\nimport warnings\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom matplotlib import rcParams\nfrom IPython.display import IFrame\nfrom IPython.core.display import display, HTML\nimport imageio\n\nfrom mpl_toolkits import mplot3d\nimport seaborn as sns\nfrom tqdm import tqdm\nfrom itertools import chain\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import losses\nfrom tensorflow.keras import models\nfrom tensorflow.keras import callbacks\n\nimport matplotlib\nmatplotlib.rcParams['animation.html'] = 'jshtml'\nfrom PIL import Image\nimport cv2\nimport glob\nimport re\nimport random\nfrom scipy import ndimage, misc\n\nimport time\nimport cv2\nimport pydicom\nfrom multiprocessing import Pool\nfrom matplotlib.animation import FuncAnimation\n\nprint(tf.__version__)\nprint(keras.__version__)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-05-23T08:33:56.039540Z","iopub.execute_input":"2022-05-23T08:33:56.040304Z","iopub.status.idle":"2022-05-23T08:33:58.483545Z","shell.execute_reply.started":"2022-05-23T08:33:56.040168Z","shell.execute_reply":"2022-05-23T08:33:58.482602Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Print filenames to observe formats\nallInputs = os.walk('/kaggle/input')\n# print(type(walking)) #\ncount = 0\nfor root, dirs, filenames in allInputs:\n for filename in filenames:\n count+=1\n if(count > 10):\n break\n print(os.path.join(root, filename))\n if(count > 10):\n break","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.485055Z","iopub.execute_input":"2022-05-23T08:33:58.485313Z","iopub.status.idle":"2022-05-23T08:33:58.495860Z","shell.execute_reply.started":"2022-05-23T08:33:58.485284Z","shell.execute_reply":"2022-05-23T08:33:58.494866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Configurations\nconfig = {\n 'data_path': '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification',\n 'train_data_path': '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train',\n 'test_data_path': '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/test',\n 'input_path': '/kaggle/input'\n}","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.497189Z","iopub.execute_input":"2022-05-23T08:33:58.497501Z","iopub.status.idle":"2022-05-23T08:33:58.506356Z","shell.execute_reply.started":"2022-05-23T08:33:58.497469Z","shell.execute_reply":"2022-05-23T08:33:58.505487Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Folder Stucture and Simple Counting\n> 1. illustrating the folder structure of training data folder\n> 2. Quick note on the level of balance of input data\n> 3. try sort the paths stored in a random folder of images\n> 4. find out if there are any potential problems in the filenames of these images\n> 5. find out whether the images are named strictly consecutively","metadata":{}},{"cell_type":"markdown","source":"### Define a Helper Function to print out structured files","metadata":{}},{"cell_type":"code","source":"# Adapted a brilliant code snippet from StackOverFlow:\n# https://stackoverflow.com/questions/9727673/list-directory-tree-structure-in-python\n\n# This function will print the first five sub-folders / sub files inside the parameter \"startpath\"\ndef list_files(startpath, num):\n masterCount = 0\n for root, dirs, files in os.walk(startpath):\n level = root.replace(startpath, '').count(os.sep)\n indent = ' ' * 4 * (level)\n masterCount += 1\n if(masterCount >= 12):\n break\n print('{}{}/'.format(indent, os.path.basename(root)))\n subindent = ' ' * 4 * (level + 1)\n count = 0\n for f in files:\n count += 1\n if(count> num):\n break\n print('{}{}'.format(subindent, f))","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.508319Z","iopub.execute_input":"2022-05-23T08:33:58.509336Z","iopub.status.idle":"2022-05-23T08:33:58.517910Z","shell.execute_reply.started":"2022-05-23T08:33:58.509296Z","shell.execute_reply":"2022-05-23T08:33:58.517045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"list_files(config['train_data_path'], 3)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.519108Z","iopub.execute_input":"2022-05-23T08:33:58.519685Z","iopub.status.idle":"2022-05-23T08:33:58.551208Z","shell.execute_reply.started":"2022-05-23T08:33:58.519645Z","shell.execute_reply":"2022-05-23T08:33:58.550581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv(os.path.join(config['data_path'],'train_labels.csv'))\nprint(\"Total number of training data points (number of patient cases): \", len(train_df))\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.552393Z","iopub.execute_input":"2022-05-23T08:33:58.552739Z","iopub.status.idle":"2022-05-23T08:33:58.570702Z","shell.execute_reply.started":"2022-05-23T08:33:58.552709Z","shell.execute_reply":"2022-05-23T08:33:58.570065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Labels (MGMT_value) are either 0 and 1\nplt.figure(figsize=(5, 5))\nsns.countplot(data=train_df, x=\"MGMT_value\")\n\n# Balanced distribution\n# Note : MGMT_value=0 does not mean there is no tumor!!!","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.571750Z","iopub.execute_input":"2022-05-23T08:33:58.572097Z","iopub.status.idle":"2022-05-23T08:33:58.740949Z","shell.execute_reply.started":"2022-05-23T08:33:58.572066Z","shell.execute_reply":"2022-05-23T08:33:58.740196Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Can we trust the integrity and organization of the images in each subfolder of each patient?\n> - Iterate through the whole folder structure and check for problems\n> - Using tqdm to show the progress","metadata":{}},{"cell_type":"code","source":"### Define a Helper Function\n# This is directly adapted from this brilliant kernel https://www.kaggle.com/mikecho/rsna-miccai-monai-ensemble/notebook \ndef natural_sort(li): \n convert = lambda text: int(text) if text.isdigit() else text.lower()\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n return sorted(li, key=alphanum_key)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.742464Z","iopub.execute_input":"2022-05-23T08:33:58.743170Z","iopub.status.idle":"2022-05-23T08:33:58.749032Z","shell.execute_reply.started":"2022-05-23T08:33:58.743095Z","shell.execute_reply":"2022-05-23T08:33:58.747933Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Note :\n> - filenames in folders can be continuous even if they don't start at \"Image-1.dcm\". It happens several times throughout the dataset.\n> - For example: \"/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00547/T2w/Image-258.dcm\" was the first image in that folder... weird.","metadata":{}},{"cell_type":"code","source":"imgFolders = glob.glob(config['train_data_path']+\"*/*/*\")\nfor i in range(5):\n print(imgFolders[i])\n\nprint(\"Do we have four sub-folders per case?:\")\nprint(len(imgFolders) == 4*len(train_df))\nfor folder in tqdm(imgFolders):\n filenames = glob.glob(folder+\"/*\")\n flag = True\n for filename in natural_sort(filenames):\n assert \"Image-\" in filename and \".dcm\" in filename\n if(flag):\n count = int(filename[7+len(folder):-4])\n flag = False\n# print(filename[7+len(folder):-4])\n if(count == int(filename[7+len(folder):-4])):\n count += 1\n else:\n print(\"there was a discontinuity, no file: \"+filename)\n break\n \n# for (root, dirs, filenames) in tqdm(os.walk(config['train_data_path'])):\n# # print(root)\n# count = 0\n# print(root)\n# for filename in filenames:\n# count +=1\n# if(count>2):\n# break\n# print(os.path.join(root, filename))","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:33:58.750735Z","iopub.execute_input":"2022-05-23T08:33:58.751079Z","iopub.status.idle":"2022-05-23T08:34:04.094496Z","shell.execute_reply.started":"2022-05-23T08:33:58.751032Z","shell.execute_reply":"2022-05-23T08:34:04.093455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### There are indeed discontinuities in the filenames!!\n> For now, skip and will revisit them later after having defined a good image loader for DICOM images","metadata":{}},{"cell_type":"markdown","source":"# Data Loader and Pre-processor\n## DICOM MetaData\n\n**Here we try to find out whether there are any useful metadata**\n\n**This code snippets is adapted from Peter's post https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/252942**\n\n#### Three axis to take the MRI scans below:\n![Brain_Axes.png](attachment:190c8bd5-befc-403c-8014-7caf5e863c5c.png)","metadata":{},"attachments":{"190c8bd5-befc-403c-8014-7caf5e863c5c.png":{"image/png":"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"}}},{"cell_type":"code","source":"args={}\nargs['input'] = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'\nargs['output'] = './'\nargs['dataset'] = 'train'\nargs['n_jobs'] = 20\nargs['debug'] = 0\n\n\nFIELDS = [\n 'AccessionNumber',\n 'AcquisitionMatrix',\n# 'B1rms', # Empty\n# 'BitsAllocated', # = 16\n# 'BitsStored', # = 16\n 'Columns',\n 'ConversionType',\n# 'DiffusionBValue', # 0 or empty\n# 'DiffusionGradientOrientation', # [0.0, 0.0, 0.0] or empty\n 'EchoNumbers',\n# 'EchoTime', # empty\n 'EchoTrainLength',\n 'FlipAngle',\n# 'HighBit', # = 15\n# 'HighRRValue', # 0 or empty\n 'ImageDimensions', # 2 or epty\n 'ImageFormat',\n 'ImageGeometryType',\n 'ImageLocation',\n 'ImageOrientation',\n 'ImageOrientationPatient',\n 'ImagePosition',\n 'ImagePositionPatient',\n# 'ImageType', # ['DERIVED', 'SECONDARY']\n 'ImagedNucleus',\n 'ImagingFrequency',\n 'InPlanePhaseEncodingDirection',\n 'InStackPositionNumber',\n 'InstanceNumber',\n# 'InversionTime', # empty\n# 'Laterality', # empty\n# 'LowRRValue', # empty\n 'MRAcquisitionType',\n 'MagneticFieldStrength',\n# 'Modality', # MR\n 'NumberOfAverages',\n 'NumberOfPhaseEncodingSteps',\n 'PatientID',\n 'PatientName',\n# 'PatientPosition', # HFS\n 'PercentPhaseFieldOfView',\n 'PercentSampling',\n# 'PhotometricInterpretation', # MONOCHROME2\n 'PixelBandwidth',\n# 'PixelPaddingValue', # empty or 0\n 'PixelRepresentation',\n 'PixelSpacing',\n# 'PlanarConfiguration', # 0 or empty\n# 'PositionReferenceIndicator', # 'NA' or empty\n 'PresentationLUTShape',\n 'ReconstructionDiameter',\n# 'RescaleIntercept', # = 0\n# 'RescaleSlope', # = 1\n# 'RescaleType', # = US\n 'Rows',\n 'SAR',\n 'SOPClassUID',\n 'SOPInstanceUID',\n# 'SamplesPerPixel', # = 1\n 'SeriesDescription',\n 'SeriesInstanceUID',\n 'SeriesNumber',\n 'SliceLocation',\n 'SliceThickness',\n 'SpacingBetweenSlices',\n 'SpatialResolution',\n 'SpecificCharacterSet',\n 'StudyInstanceUID',\n# 'TemporalResolution', # 0 or empty\n# 'TransferSyntaxUID', # = 1.2.840.10008.1.2\n# 'TriggerWindow', # = 0\n 'WindowCenter',\n 'WindowWidth'\n]\n\n# All of the FM fields are empty\nFM_FIELDS = [\n 'FileMetaInformationGroupLength',\n 'FileMetaInformationVersion',\n 'ImplementationClassUID',\n 'ImplementationVersionName',\n 'MediaStorageSOPClassUID',\n 'MediaStorageSOPInstanceUID',\n 'SourceApplicationEntityTitle',\n 'TransferSyntaxUID',\n]\n\nfinal = []\n\n\ndef get_meta_info(dicom):\n row = {f: dicom.get(f) for f in FIELDS}\n row_fm = {f: dicom.file_meta.get(f) for f in FM_FIELDS}\n row_other = {\n# 'is_original_encoding': dicom.is_original_encoding, # = True\n# 'is_implicit_VR': dicom.is_implicit_VR, # = True\n# 'is_little_endian': dicom.is_little_endian, # = True\n 'timestamp': dicom.timestamp,\n }\n return {**row,\n #**row_fm, # All are emtpy\n **row_other}\n\n\ndef get_dicom_files(input_dir, ds='train'):\n dicoms = []\n\n for subdir, dirs, files in os.walk(f\"{input_dir}/{ds}\"):\n for filename in files:\n filepath = subdir + os.sep + filename\n\n if filepath.endswith(\".dcm\"):\n dicoms.append(filepath)\n\n return dicoms\n\n\ndef process_dicom(dicom_src, _x):\n dicom = pydicom.dcmread(dicom_src)\n file_data = dicom_src.split(\"/\")\n file_src = \"/\".join(file_data[-4:])\n\n tmp = {\"BraTS21ID\": file_data[-3], \"dataset\": file_data[-4], \"type\": file_data[-2], \"dicom_src\": f\"./{file_src}\"}\n tmp.update(get_meta_info(dicom))\n\n return tmp\n\n\ndef update(res):\n if res is not None:\n final.append(res)\n\n pbar.update()\n\n\ndef error(e):\n print(e)\n\n\n# # Actually run this file\n# dicom_files = get_dicom_files(args[\"input\"], args[\"dataset\"])\n\n# if args[\"debug\"]:\n# dicom_files = dicom_files[:1000]\n\n# pool = Pool(processes=args[\"n_jobs\"])\n# pbar = tqdm(total=len(dicom_files))\n\n# for dicom_file in dicom_files:\n# pool.apply_async(\n# process_dicom,\n# args=(dicom_file, ''),\n# callback=update,\n# error_callback=error,\n# )\n\n# pool.close()\n# pool.join()\n# pbar.close()\n\n# final = pd.DataFrame(final)\n# final.to_csv(f\"{args['output']}/dicom_meta_{args['dataset']}.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.098318Z","iopub.execute_input":"2022-05-23T08:34:04.098900Z","iopub.status.idle":"2022-05-23T08:34:04.115682Z","shell.execute_reply.started":"2022-05-23T08:34:04.098847Z","shell.execute_reply":"2022-05-23T08:34:04.114676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### A function to get four images fileNames (each of one type) for one patient","metadata":{}},{"cell_type":"code","source":"def images_paths_from_patientID(BraTS21ID, imgs_per_folder = 1):\n patientFolderPath = os.path.join(config['train_data_path'],str(BraTS21ID).zfill(5))\n imgList = []\n \n # get the name of the four sub-folders\n for dir in glob.glob(os.path.join(patientFolderPath,\"*\")):\n for i in range(imgs_per_folder):\n imgFileName = random.choice(glob.glob(os.path.join(dir,\"*\")))\n imgList.append(imgFileName)\n return imgList\n\nprint(images_paths_from_patientID(324, imgs_per_folder = 2))","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.117187Z","iopub.execute_input":"2022-05-23T08:34:04.117592Z","iopub.status.idle":"2022-05-23T08:34:04.149122Z","shell.execute_reply.started":"2022-05-23T08:34:04.117543Z","shell.execute_reply":"2022-05-23T08:34:04.148332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### A function to get the image plane from DICOM file\n**This is directly adopted from this brilliant notebook: https://www.kaggle.com/arnabs007/part-1-rsna-miccai-btrc-understanding-the-data**","metadata":{}},{"cell_type":"code","source":"# https://www.kaggle.com/arnabs007/part-1-rsna-miccai-btrc-understanding-the-data\ndef get_image_plane(dicomFile):\n '''\n Returns the MRI's plane from the dicom data.\n \n '''\n# print(dicomFile.get(\"ImageOrientationPatient\"))\n x1,y1,_,x2,y2,_ = [round(j) for j in dicomFile.get(\"ImageOrientationPatient\")]\n cords = [x1,y1,x2,y2]\n\n if cords == [1,0,0,0]:\n return 'coronal'\n if cords == [1,0,0,1]:\n return 'axial'\n if cords == [0,1,0,0]:\n return 'sagittal'","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.150298Z","iopub.execute_input":"2022-05-23T08:34:04.151175Z","iopub.status.idle":"2022-05-23T08:34:04.157140Z","shell.execute_reply.started":"2022-05-23T08:34:04.151114Z","shell.execute_reply":"2022-05-23T08:34:04.156476Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### A function to display 4*n images (with axis information)","metadata":{}},{"cell_type":"code","source":"def display_img_from_file(fileNames, resize = True):\n num = len(fileNames)\n print(num)\n fig, axs = plt.subplots(num//4, 4, sharex=True, sharey=True, figsize=(15,15))\n count = 0\n for img in fileNames:\n# print(img)\n image = pydicom.dcmread(img)\n plane = get_image_plane(image)\n \n if resize:\n imageArr = cv2.resize(image.pixel_array, (256,256))\n else:\n imageArr = image.pixel_array\n\n ser = img.split(\"/\")\n title = ser[-2] + \" - \" + plane + \" | size: \" + str(imageArr.shape)\n axs[count//4, count%4].title.set_text(title)\n axs[count//4, count%4].imshow(imageArr, cmap='gray')\n axs[count//4, count%4].axis(\"off\")\n count += 1","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.158231Z","iopub.execute_input":"2022-05-23T08:34:04.158690Z","iopub.status.idle":"2022-05-23T08:34:04.170444Z","shell.execute_reply.started":"2022-05-23T08:34:04.158657Z","shell.execute_reply":"2022-05-23T08:34:04.169427Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Combine them and show some sample images","metadata":{}},{"cell_type":"code","source":"patientIDs = train_df[\"BraTS21ID\"].sample(n=3).to_numpy()\nprint(patientIDs)\nfileNames = []\n\nfor patientID in patientIDs:\n for fileName in images_paths_from_patientID(patientID, 2):\n fileNames.append(fileName)\n \nprint(len(fileNames))","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.171740Z","iopub.execute_input":"2022-05-23T08:34:04.172158Z","iopub.status.idle":"2022-05-23T08:34:04.206410Z","shell.execute_reply.started":"2022-05-23T08:34:04.172086Z","shell.execute_reply":"2022-05-23T08:34:04.205432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Further Data Manipulation\n> Define a helper function\n\n**Display:**\n> 1. Original image shape**\n> 2. np.mean**\n> 3. range of values**","metadata":{}},{"cell_type":"code","source":"# Takes a list of fileName (specific to \"*/*/*.dcm\") and print metadata and original image\ndef display_img_from_file(fileNames, showHist = False):\n num = len(fileNames)\n print(num)\n fig, axs = plt.subplots(num//4, 4, sharex=True, sharey=True, figsize=(15,15))\n fig.subplots_adjust(left=None, bottom=None, right=None, top=1.25, wspace=None, hspace=None)\n count = 0\n \n for img in fileNames:\n \n image = pydicom.dcmread(img)\n plane = get_image_plane(image)\n image = image.pixel_array\n \n # display metadata in title\n ser = img.split(\"/\")\n title = ser[-2] + \" - \" + plane + \" | size: \" + str(image.shape)\n title += \"\\n\" + \"data type:\" + str(image.dtype)\n title += \"\\n\" + \"range: \" + str(np.amax(image) - np.amin(image))\n title += \" mean: \" + \"{:.2f}\".format(np.mean(image))\n \n axs[count//4, count%4].title.set_text(title)\n if(not showHist):\n axs[count//4, count%4].imshow(image, cmap='gray')\n else:\n histogram, bin_edges = np.histogram(image, bins=256)\n axs[count//4, count%4].plot(bin_edges[:-1], histogram)\n \n axs[count//4, count%4].axis(\"off\")\n count += 1 ","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.207721Z","iopub.execute_input":"2022-05-23T08:34:04.208476Z","iopub.status.idle":"2022-05-23T08:34:04.219973Z","shell.execute_reply.started":"2022-05-23T08:34:04.208423Z","shell.execute_reply":"2022-05-23T08:34:04.219235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_img_from_file(fileNames)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:04.221279Z","iopub.execute_input":"2022-05-23T08:34:04.222264Z","iopub.status.idle":"2022-05-23T08:34:06.647899Z","shell.execute_reply.started":"2022-05-23T08:34:04.222223Z","shell.execute_reply":"2022-05-23T08:34:06.647201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### These pictures need to be normalized and correctly aligned!\n> Otherwise, we can see that there range, mean, and even size vary a lot","metadata":{}},{"cell_type":"markdown","source":"# Image Pre-processing\n> - reconstruct to 3D and then re-sample\n> - resize the images\n> - normalize and standardize the pixel values\n> - auto center-cropping","metadata":{}},{"cell_type":"markdown","source":"### Reconstruct to 3D\n**Adapted from this wonderful notebook: https://www.kaggle.com/sreevishnudamodaran/rsna-3d-clahe-voxels-tpu-3d-augmentations**","metadata":{}},{"cell_type":"code","source":"def get_voxel(patientFolder, scan_type):\n \n dcm_dir = os.path.join(patientFolder, scan_type)\n# dcm_dir = os.path.join(config[\"train\"], study_id, scan_type)\n# dcm_dir = data_root.joinpath(DATASET, study_id, scan_type)\n\n # This is a naive way of sorting, not based on metadata\n# dcm_paths = sorted(glob.glob(dcm_dir + \"/*.dcm\"), key=lambda x: int(x.stem.split(\"-\")[-1]))\n dcm_paths = natural_sort(glob.glob(dcm_dir + \"/*.dcm\"))\n \n imgs = []\n positions = []\n \n size = -1\n for dcm_path in dcm_paths:\n img = pydicom.dcmread(str(dcm_path))\n imgs.append(img.pixel_array)\n if size == -1:\n size = img.pixel_array.shape\n elif size != img.pixel_array.shape:\n print(\"Inconsistent size of image: \" + str(img.pixel_array.shape))\n\n positions.append(img.ImagePositionPatient)\n \n plane = get_image_plane(img)\n # Notice that all images are supposed to have the same size\n voxel = np.stack(imgs)\n \n # reorder planes if needed and rotate voxel\n # This is also a naive way to determine if the order of images need to be re-ordered.\n if plane == \"coronal\":\n if positions[0][1] < positions[-1][1]:\n voxel = voxel[::-1]\n print(f\"{dcm_dir[-9:]} {scan_type} {plane} reordered\")\n voxel = voxel.transpose((1, 0, 2))\n elif plane == \"sagittal\":\n if positions[0][0] < positions[-1][0]:\n voxel = voxel[::-1]\n print(f\"{dcm_dir[-9:]} {scan_type} {plane} reordered\")\n voxel = voxel.transpose((1, 2, 0))\n voxel = np.rot90(voxel, 2, axes=(1, 2))\n elif plane == \"axial\":\n if positions[0][2] > positions[-1][2]:\n voxel = voxel[::-1]\n print(f\"{dcm_dir[-9:]} {scan_type} {plane} reordered\")\n voxel = np.rot90(voxel, 2)\n else:\n raise ValueError(f\"Unknown plane {plane}\")\n# return voxel, plane\n return voxel","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:06.649231Z","iopub.execute_input":"2022-05-23T08:34:06.649899Z","iopub.status.idle":"2022-05-23T08:34:06.661356Z","shell.execute_reply.started":"2022-05-23T08:34:06.649861Z","shell.execute_reply":"2022-05-23T08:34:06.660126Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def normalize_contrast(voxel):\n if voxel.sum() == 0:\n return voxel\n voxel = voxel - np.min(voxel)\n voxel = voxel / np.max(voxel)\n voxel = (voxel * 255).astype(np.uint8)\n return voxel","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:06.662697Z","iopub.execute_input":"2022-05-23T08:34:06.663346Z","iopub.status.idle":"2022-05-23T08:34:06.678821Z","shell.execute_reply.started":"2022-05-23T08:34:06.663302Z","shell.execute_reply":"2022-05-23T08:34:06.678024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def crop_voxel(voxel):\n if voxel.sum() == 0:\n return voxel\n keep = (voxel.mean(axis=(0, 1)) > 0)\n voxel = voxel[:, :, keep]\n keep = (voxel.mean(axis=(0, 2)) > 0)\n voxel = voxel[:, keep, :]\n keep = (voxel.mean(axis=(1, 2)) > 0)\n voxel = voxel[keep, :, :]\n return voxel","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:06.680047Z","iopub.execute_input":"2022-05-23T08:34:06.680522Z","iopub.status.idle":"2022-05-23T08:34:06.696341Z","shell.execute_reply.started":"2022-05-23T08:34:06.680471Z","shell.execute_reply":"2022-05-23T08:34:06.695377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Showcasing our 3D-reconstruction works","metadata":{}},{"cell_type":"code","source":"voxel = get_voxel(\"/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00688\", \"T2w\")","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:06.697588Z","iopub.execute_input":"2022-05-23T08:34:06.697850Z","iopub.status.idle":"2022-05-23T08:34:07.837161Z","shell.execute_reply.started":"2022-05-23T08:34:06.697817Z","shell.execute_reply":"2022-05-23T08:34:07.836421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(type(voxel))\nprint(voxel.mean())\nprint(voxel.shape)\n\nprint(\"\\nNow crop this voxel \\n\")\nvoxel = crop_voxel(voxel)\nprint(voxel.mean())\nprint(voxel.shape)\n\nprint(\"\\nNow Downsample this voxel \\n\")\nx = voxel.shape[0]\ny = voxel.shape[1]\nz = voxel.shape[2]\n\ndownsampled_voxel = ndimage.zoom(voxel, (32/x, 24/y, 34/z))\nprint(downsampled_voxel.mean())\nprint(downsampled_voxel.shape)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:07.838586Z","iopub.execute_input":"2022-05-23T08:34:07.839063Z","iopub.status.idle":"2022-05-23T08:34:11.107864Z","shell.execute_reply.started":"2022-05-23T08:34:07.839026Z","shell.execute_reply":"2022-05-23T08:34:11.106957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# after stacking along axis = 0, the z is supposed to be in the front\nz,x,y = downsampled_voxel.nonzero()\nfig = plt.figure(figsize=(15, 15))\nax = fig.add_subplot(111, projection='3d')\nax.set_xlabel('X Label')\nax.set_ylabel('Y Label')\nax.set_zlabel('Z Label')\n\nax.scatter(x, y, z)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:11.109081Z","iopub.execute_input":"2022-05-23T08:34:11.109377Z","iopub.status.idle":"2022-05-23T08:34:11.606245Z","shell.execute_reply.started":"2022-05-23T08:34:11.109344Z","shell.execute_reply":"2022-05-23T08:34:11.605227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Test our get_voxel() method, see if it helps (new random patient)","metadata":{}},{"cell_type":"code","source":"# Randomly selects one patient and displays the origianl / unprocessed images \npatientIDs = train_df[\"BraTS21ID\"].sample(n=1).to_numpy()\nprint(patientIDs)\nfileNames = []\n\n# Get the filenames\nfor patientID in patientIDs:\n for fileName in images_paths_from_patientID(patientID, 5): # 4 subfolders, 5 imgs from each = 20 total imgs\n fileNames.append(fileName)\nprint(len(fileNames))","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:11.607553Z","iopub.execute_input":"2022-05-23T08:34:11.607802Z","iopub.status.idle":"2022-05-23T08:34:11.646636Z","shell.execute_reply.started":"2022-05-23T08:34:11.607772Z","shell.execute_reply":"2022-05-23T08:34:11.645865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Here's what they originally look like","metadata":{}},{"cell_type":"code","source":"display_img_from_file(fileNames)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:11.647769Z","iopub.execute_input":"2022-05-23T08:34:11.647981Z","iopub.status.idle":"2022-05-23T08:34:13.240641Z","shell.execute_reply.started":"2022-05-23T08:34:11.647955Z","shell.execute_reply":"2022-05-23T08:34:13.239652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"patientIDs[0]\nprint(type(patientIDs[0]))\npatientFolder = \"/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train/\" + str(patientIDs[0]).zfill(5)\nprint(patientFolder)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:13.242121Z","iopub.execute_input":"2022-05-23T08:34:13.242379Z","iopub.status.idle":"2022-05-23T08:34:13.248658Z","shell.execute_reply.started":"2022-05-23T08:34:13.242350Z","shell.execute_reply":"2022-05-23T08:34:13.247565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load all four subfolders of this specific patient\nvoxelT2w = get_voxel(patientFolder, \"T2w\")\nvoxelT1wCE = get_voxel(patientFolder, \"T1wCE\")\nvoxelT1w = get_voxel(patientFolder, \"T1w\")\nvoxelFLAIR = get_voxel(patientFolder, \"FLAIR\")","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:13.250273Z","iopub.execute_input":"2022-05-23T08:34:13.250630Z","iopub.status.idle":"2022-05-23T08:34:18.124291Z","shell.execute_reply.started":"2022-05-23T08:34:13.250581Z","shell.execute_reply":"2022-05-23T08:34:18.123462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check basic dimensions\ndef check_basic_info():\n print(type(voxelT2w))\n print(voxelT2w.mean())\n print(voxelT2w.shape)\n\n print(type(voxelT1wCE))\n print(voxelT1wCE.mean())\n print(voxelT1wCE.shape)\n\n print(type(voxelT1w))\n print(voxelT1w.mean())\n print(voxelT1w.shape)\n\n print(type(voxelFLAIR))\n print(voxelFLAIR.mean())\n print(voxelFLAIR.shape)\n\ncheck_basic_info()","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:18.125532Z","iopub.execute_input":"2022-05-23T08:34:18.125819Z","iopub.status.idle":"2022-05-23T08:34:18.157818Z","shell.execute_reply.started":"2022-05-23T08:34:18.125788Z","shell.execute_reply":"2022-05-23T08:34:18.156774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Crop, normalize, resize and check again","metadata":{}},{"cell_type":"code","source":"def resize_interpol(voxel, size = (128, 128, 128)):\n x = voxel.shape[0]\n y = voxel.shape[1]\n z = voxel.shape[2]\n\n downsampled_voxel = ndimage.zoom(voxel, (size[0]/x, size[1]/y, size[2]/z))\n return downsampled_voxel","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:18.162284Z","iopub.execute_input":"2022-05-23T08:34:18.162970Z","iopub.status.idle":"2022-05-23T08:34:18.169361Z","shell.execute_reply.started":"2022-05-23T08:34:18.162931Z","shell.execute_reply":"2022-05-23T08:34:18.167939Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"voxelT2w = crop_voxel(voxelT2w)\nvoxelT2w = resize_interpol(voxelT2w)\nvoxelT2w = normalize_contrast(voxelT2w)\n\nvoxelT1wCE = crop_voxel(voxelT1wCE)\nvoxelT1wCE = resize_interpol(voxelT1wCE)\nvoxelT1wCE = normalize_contrast(voxelT1wCE)\n\nvoxelT1w = crop_voxel(voxelT1w)\nvoxelT1w = resize_interpol(voxelT1w)\nvoxelT1w = normalize_contrast(voxelT1w)\n\nvoxelFLAIR = crop_voxel(voxelFLAIR)\nvoxelFLAIR = resize_interpol(voxelFLAIR)\nvoxelFLAIR = normalize_contrast(voxelFLAIR)\n\ncheck_basic_info()","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:18.170743Z","iopub.execute_input":"2022-05-23T08:34:18.171077Z","iopub.status.idle":"2022-05-23T08:34:22.459899Z","shell.execute_reply.started":"2022-05-23T08:34:18.171030Z","shell.execute_reply":"2022-05-23T08:34:22.458911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Display some images to see how much improvements can be made","metadata":{}},{"cell_type":"code","source":"# https://www.kaggle.com/sreevishnudamodaran/rsna-3d-clahe-voxels-tpu-3d-augmentations\ndef show_animation(images, normalized=True):\n fig = plt.figure(figsize=(4, 4))\n plt.axis('off')\n if normalized:\n im = plt.imshow(images[0], cmap='gray', vmin=0.0, vmax=1.0)\n else:\n print(\"Not normalized\")\n im = plt.imshow(images[0], cmap='gray', vmin=0.0, vmax=255.0)\n def animate(i):\n im.set_array(images[i])\n # return the artists set\n return [im]\n display(FuncAnimation(fig, animate, frames=len(images),\n interval=50))\n plt.close()\n\nshow_animation(voxelT1w, False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:22.461262Z","iopub.execute_input":"2022-05-23T08:34:22.461576Z","iopub.status.idle":"2022-05-23T08:34:25.964166Z","shell.execute_reply.started":"2022-05-23T08:34:22.461533Z","shell.execute_reply":"2022-05-23T08:34:25.962731Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_animation(voxelT2w, False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:25.966166Z","iopub.execute_input":"2022-05-23T08:34:25.966986Z","iopub.status.idle":"2022-05-23T08:34:29.560347Z","shell.execute_reply.started":"2022-05-23T08:34:25.966940Z","shell.execute_reply":"2022-05-23T08:34:29.559259Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_animation(voxelT1wCE, False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:29.562072Z","iopub.execute_input":"2022-05-23T08:34:29.562931Z","iopub.status.idle":"2022-05-23T08:34:32.997582Z","shell.execute_reply.started":"2022-05-23T08:34:29.562882Z","shell.execute_reply":"2022-05-23T08:34:32.996602Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_animation(voxelFLAIR, False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:32.999940Z","iopub.execute_input":"2022-05-23T08:34:33.000246Z","iopub.status.idle":"2022-05-23T08:34:36.656340Z","shell.execute_reply.started":"2022-05-23T08:34:33.000208Z","shell.execute_reply":"2022-05-23T08:34:36.654911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Now Re-sample down\n\n> - Important Questions\n> Which axis should we be cropping against?","metadata":{}},{"cell_type":"code","source":"# axis = 0 should be cropping at ? axis?\n\n# Distance -> sampling distance. Pick one img from every stack of 5 images\n# Number of images output = original number of images on axis / sampling distance\n# Skip_imgs -> skip the first and last few selected images (as they are probably not useful anyway)\ndef resample_from_voxel(voxel, axis = 0, distance = 3, skip_imgs = 3):\n output_imgs = []\n counter = 0\n for i in range(voxel.shape[axis]//distance):\n if axis == 0:\n output_imgs.append(voxel[counter, :, :])\n# print(\"added image\")\n elif axis == 1:\n output_imgs.append(voxel[:, counter, :])\n elif axis ==2:\n output_imgs.append(voxel[:, :, counter])\n counter += distance\n \n output = np.array(output_imgs[skip_imgs: -skip_imgs])\n return output","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:36.657633Z","iopub.execute_input":"2022-05-23T08:34:36.657882Z","iopub.status.idle":"2022-05-23T08:34:36.664015Z","shell.execute_reply.started":"2022-05-23T08:34:36.657851Z","shell.execute_reply":"2022-05-23T08:34:36.663396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# The size shows that cropping works.\noutputT2w = resample_from_voxel(voxelT2w, distance = 2,axis = 0)\noutputT1wCE = resample_from_voxel(voxelT1wCE, axis = 0)\noutputT1w = resample_from_voxel(voxelT1w, axis = 0)\noutputFLAIR = resample_from_voxel(voxelFLAIR, axis = 0)\n\nprint(outputT2w.shape)\nprint(outputT1wCE.shape)\nprint(outputT1w.shape)\nprint(outputFLAIR.shape)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:36.665115Z","iopub.execute_input":"2022-05-23T08:34:36.665379Z","iopub.status.idle":"2022-05-23T08:34:36.680147Z","shell.execute_reply.started":"2022-05-23T08:34:36.665323Z","shell.execute_reply":"2022-05-23T08:34:36.679099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_animation(outputT2w, False)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:36.681840Z","iopub.execute_input":"2022-05-23T08:34:36.682084Z","iopub.status.idle":"2022-05-23T08:34:38.347248Z","shell.execute_reply.started":"2022-05-23T08:34:36.682053Z","shell.execute_reply":"2022-05-23T08:34:38.346238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# display imgs from data of shape (x,y,z)\ndef print_imgs_from_numpy_data(data, num = 10):\n\n fig, axs = plt.subplots(num//5, 5, sharex=True, sharey=True, figsize=(15,15))\n# fig.subplots_adjust(left=None, bottom=None, right=None, top=1.25, wspace=None, hspace=None)\n \n sample_rate = data.shape[0]//num\n print(sample_rate)\n count = 0\n img_num = 0\n \n for i in range(num):\n image = data[count,:,:]\n assert len(image.shape) == 2\n axs[img_num//5, count%5].imshow(image, cmap='gray')\n title = \"\"\n title += \"\\n\" + \"data type:\" + str(image.dtype)\n title += \"\\n\" + \"range: \" + str(np.amax(image) - np.amin(image))\n title += \" mean: \" + \"{:.2f}\".format(np.mean(image))\n \n axs[img_num//5, count%5].title.set_text(title)\n axs[img_num//5, count%5].axis(\"off\")\n count += sample_rate\n img_num+=1","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:38.348918Z","iopub.execute_input":"2022-05-23T08:34:38.349200Z","iopub.status.idle":"2022-05-23T08:34:38.357680Z","shell.execute_reply.started":"2022-05-23T08:34:38.349164Z","shell.execute_reply":"2022-05-23T08:34:38.356246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print_imgs_from_numpy_data(outputT1wCE)","metadata":{"execution":{"iopub.status.busy":"2022-05-23T08:34:38.359315Z","iopub.execute_input":"2022-05-23T08:34:38.359666Z","iopub.status.idle":"2022-05-23T08:34:39.006399Z","shell.execute_reply.started":"2022-05-23T08:34:38.359624Z","shell.execute_reply":"2022-05-23T08:34:39.005681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Actual Data Loader","metadata":{}}]}