{"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":"# 🧠Brain Tumor 3D EDA","metadata":{}},{"cell_type":"markdown","source":"* **The goal:**In this competition you will predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test your model to detect for the presence of MGMT promoter methylation.","metadata":{}},{"cell_type":"markdown","source":"# Data Description\n* The competition data is defined by three cohorts: **Training**, **Validation** (Public), and **Testing** (Private). The “Training” and the “Validation” cohorts are provided to the participants, whereas the “Testing” cohort is kept hidden at all times, during and after the competition.\n\n* These 3 cohorts are structured as follows: Each independent case has a dedicated folder identified by a five-digit number. Within each of these “case” folders, there are four sub-folders, each of them corresponding to each of the structural multi-parametric MRI (mpMRI) scans, in DICOM format.\n\n## .dcm\nThe .dcm file is DICOM (Digital Imaging and Communications in Medicine), which is a file for recording medical images and related information in medical digital imaging and communication. When used for medical image processing, we need to read the image information in the .dcm file into the python program.","metadata":{}},{"cell_type":"markdown","source":"## The exact mpMRI scans included are:\n1.  Fluid Attenuated Inversion Recovery (FLAIR)\n1. T1-weighted pre-contrast (T1w)\n1. T1-weighted post-contrast (T1Gd)\n1. T2-weighted (T2)\n\n**Exact folder structure:**","metadata":{}},{"cell_type":"markdown","source":"![1q.jpg](attachment:f2c0f2f5-d6ae-4ee8-b765-e0661b305049.jpg)","metadata":{},"attachments":{"f2c0f2f5-d6ae-4ee8-b765-e0661b305049.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"## Files\n* **train** folder containing the training files, with each top-level folder representing a subject\n* **train_labels.csv** file containing the target MGMT_value for each subject in the training data (e.g. the presence of MGMT promoter methylation)\n* **test** the test files, which use the same structure as train/; your task is to predict the MGMT_value for each subject in the test data. NOTE: the total size of the rerun test set (Public and Private) is ~5x the size of the Public test set\n* **sample_submission.csv**  a sample submission file in the correct format","metadata":{}},{"cell_type":"markdown","source":"# importing libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport os\nimport json\nimport glob\nimport random\nimport collections\nimport cv2\n\nimport pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\n\nfrom matplotlib import animation, rc\nrc('animation', html='jshtml')\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:49.610303Z","iopub.execute_input":"2021-07-22T09:33:49.610976Z","iopub.status.idle":"2021-07-22T09:33:50.972423Z","shell.execute_reply.started":"2021-07-22T09:33:49.610887Z","shell.execute_reply":"2021-07-22T09:33:50.971518Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# loading data","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv\")\ntrain_df.head(5)","metadata":{"execution":{"iopub.status.busy":"2021-07-22T09:33:50.973707Z","iopub.execute_input":"2021-07-22T09:33:50.973965Z","iopub.status.idle":"2021-07-22T09:33:51.005604Z","shell.execute_reply.started":"2021-07-22T09:33:50.973939Z","shell.execute_reply":"2021-07-22T09:33:51.004773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(5, 5))\nsns.countplot(data=train_df, x=\"MGMT_value\");","metadata":{"execution":{"iopub.status.busy":"2021-07-22T09:33:51.007219Z","iopub.execute_input":"2021-07-22T09:33:51.007479Z","iopub.status.idle":"2021-07-22T09:33:51.169181Z","shell.execute_reply.started":"2021-07-22T09:33:51.007454Z","shell.execute_reply":"2021-07-22T09:33:51.168229Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Visualization","metadata":{}},{"cell_type":"code","source":"def load_dicom(path):\n    dicom = pydicom.read_file(path)\n    data = dicom.pixel_array\n    data = data - np.min(data)\n    if np.max(data) != 0:\n        data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    return data\n\n\n\ndef visualize_sample(\n    brats21id, \n    slice_i,\n    mgmt_value,\n    types=(\"FLAIR\", \"T1w\", \"T1wCE\", \"T2w\")\n):\n    plt.figure(figsize=(10, 3))\n    patient_path = os.path.join(\n        \"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/\", \n        str(brats21id).zfill(5),\n    )\n    for i, t in enumerate(types, 1):\n        t_paths = sorted(\n            glob.glob(os.path.join(patient_path, t, \"*\")), \n            key=lambda x: int(x[:-4].split(\"-\")[-1]),\n        )\n        data = load_dicom(t_paths[int(len(t_paths) * slice_i)])\n        plt.subplot(1, 4, i)\n        plt.imshow(data, cmap=\"gray\")\n        plt.title(f\"{t}\", fontsize=10)\n        plt.axis(\"off\")\n    plt.show()\n    \n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:51.170698Z","iopub.execute_input":"2021-07-22T09:33:51.171007Z","iopub.status.idle":"2021-07-22T09:33:51.181441Z","shell.execute_reply.started":"2021-07-22T09:33:51.170976Z","shell.execute_reply":"2021-07-22T09:33:51.180521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# MGMT=0","metadata":{}},{"cell_type":"code","source":"list0=[315,176,153,164]\nfor i in list0:\n    _brats21id = train_df.iloc[i][\"BraTS21ID\"]\n    _mgmt_value = train_df.iloc[i][\"MGMT_value\"]\n    visualize_sample(brats21id=_brats21id, mgmt_value=_mgmt_value, slice_i=0.55)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:51.182896Z","iopub.execute_input":"2021-07-22T09:33:51.183324Z","iopub.status.idle":"2021-07-22T09:33:52.845588Z","shell.execute_reply.started":"2021-07-22T09:33:51.183282Z","shell.execute_reply":"2021-07-22T09:33:52.844839Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# MGMT=1","metadata":{}},{"cell_type":"code","source":"list1=[184,315,155,228]\nfor i in list1:\n    _brats21id = train_df.iloc[i][\"BraTS21ID\"]\n    _mgmt_value = train_df.iloc[i][\"MGMT_value\"]\n    visualize_sample(brats21id=_brats21id, mgmt_value=_mgmt_value, slice_i=0.55)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:52.846536Z","iopub.execute_input":"2021-07-22T09:33:52.846844Z","iopub.status.idle":"2021-07-22T09:33:54.405196Z","shell.execute_reply.started":"2021-07-22T09:33:52.846813Z","shell.execute_reply":"2021-07-22T09:33:54.404148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Animation","metadata":{}},{"cell_type":"code","source":"def create_animation(ims):\n    fig = plt.figure(figsize=(5, 5))\n    plt.axis('off')\n    im = plt.imshow(ims[0], cmap=\"gray\")\n\n    def animate_func(i):\n        im.set_array(ims[i])\n        #return [im]\n    return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//4)\n\ndef load_dicom_line(path):\n    t_paths = sorted(\n        glob.glob(os.path.join(path, \"*\")), \n        key=lambda x: int(x[:-4].split(\"-\")[-1]),\n    )\n    images = []\n    for filename in t_paths:\n        data = load_dicom(filename)\n        if data.max() == 0:\n            continue\n        images.append(data)\n        \n    return images","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:54.406469Z","iopub.execute_input":"2021-07-22T09:33:54.40676Z","iopub.status.idle":"2021-07-22T09:33:54.414488Z","shell.execute_reply.started":"2021-07-22T09:33:54.406733Z","shell.execute_reply":"2021-07-22T09:33:54.413584Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/FLAIR\")\nanm_FLAIR0=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/T1w\")\nanm_T1W0=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/T1wCE\")\nanm_T1wCE0=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/T2w\")\nanm_T2w0=create_animation(images)\n\n\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00184/FLAIR\")\nanm_FLAIR1=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00184/T1w\")\nanm_T1W1=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00184/T1wCE\")\nanm_T1wCE1=create_animation(images)\nimages = load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00184/T2w\")\nanm_T2w1=create_animation(images)","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T10:03:21.859516Z","iopub.execute_input":"2021-07-22T10:03:21.85988Z","iopub.status.idle":"2021-07-22T10:03:27.041897Z","shell.execute_reply.started":"2021-07-22T10:03:21.859847Z","shell.execute_reply":"2021-07-22T10:03:27.041136Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# MGMT=0","metadata":{}},{"cell_type":"markdown","source":"### FLAIR","metadata":{}},{"cell_type":"code","source":"anm_FLAIR0","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T09:33:56.600592Z","iopub.execute_input":"2021-07-22T09:33:56.600992Z","iopub.status.idle":"2021-07-22T09:33:58.032208Z","shell.execute_reply.started":"2021-07-22T09:33:56.600949Z","shell.execute_reply":"2021-07-22T09:33:58.031146Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# T1w","metadata":{}},{"cell_type":"code","source":"anm_T1W0","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-07-22T10:04:19.908059Z","iopub.execute_input":"2021-07-22T10:04:19.908534Z","iopub.status.idle":"2021-07-22T10:04:24.203755Z","shell.execute_reply.started":"2021-07-22T10:04:19.908503Z","shell.execute_reply":"2021-07-22T10:04:24.202332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 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"}}},{"cell_type":"markdown","source":"# References","metadata":{}},{"cell_type":"markdown","source":"1. https://www.kaggle.com/avloss/eda-with-animation\n1. https://www.kaggle.com/polomarco/visualizatio-3d-nifti-dicom-matlab-nrrd-files\n1. https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling\n1. https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling","metadata":{}},{"cell_type":"markdown","source":"## WORK IN PROGRESS...","metadata":{}}]}