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{"cells":[{"cell_type":"markdown","metadata":{"id":"Pdo15DbR87dM"},"source":["nnUNet"]},{"cell_type":"markdown","source":["Libraries"],"metadata":{"id":"NbvJ5LnvUga8"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"xUCyIomK863_"},"outputs":[],"source":["import os\n","import shutil\n","from collections import OrderedDict\n","\n","import json\n","import matplotlib.pyplot as plt\n","import nibabel as nib\n","\n","import numpy as np\n","import torch"]},{"cell_type":"markdown","source":["Google Drive Mounting"],"metadata":{"id":"YLO69939Ujrl"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive',force_remount = True)\n","\n","drive_dir = \"/content/drive/My Drive\"\n","mount_dir = os.path.join(drive_dir, \"Colab Notebooks\")\n","base_dir = os.getcwd()\n","\n","assert os.path.exists(drive_dir) # if this fails, something went wrong with mounting GoogleDrive\n","if os.path.exists(mount_dir) is False:\n","    os.makedirs(mount_dir)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VUGmGeMHZEuF","executionInfo":{"status":"ok","timestamp":1695158172362,"user_tz":-720,"elapsed":30712,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"}},"outputId":"dc74f333-d8fb-47fc-c231-f0c88cc83059"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["PyTorch GPU"],"metadata":{"id":"3e9d8DlUUmXC"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1695158172364,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"udrOG3l18_Lz","outputId":"a9af6780-bc5c-485a-f0d7-fe5a8fe4ef49"},"outputs":[{"output_type":"stream","name":"stdout","text":["GPU available: True\n","Pytorch version: 2.0.1+cu118\n","Device name: Tesla T4\n"]}],"source":["# check whether GPU accelerated computing is available\n","assert torch.cuda.is_available() # if there is an error here, enable GPU in the Runtime\n","print(\"GPU available:\", torch.cuda.is_available())\n","\n","if torch.cuda.is_available():\n","    device = torch.device(\"cuda\")\n","    print(f\"Pytorch version: {torch.__version__}\")\n","    print(f\"Device name: {torch.cuda.get_device_name(0)}\")\n","else:\n","    device = torch.device(\"cpu\")\n","    print(\"No GPU available.\")"]},{"cell_type":"markdown","source":["Folder & Data Setup"],"metadata":{"id":"OGUDvJn8Uqvn"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":37024,"status":"ok","timestamp":1695158209379,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"huyIhjS5Dqs6","outputId":"dd39c46e-cd09-4e28-eb64-b3182da634ec"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting nnunetv2\n","  Downloading nnunetv2-2.2.tar.gz (178 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/178.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/178.2 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.2/178.2 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from nnunetv2) (2.0.1+cu118)\n","Collecting acvl-utils>=0.2 (from nnunetv2)\n","  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collected packages: nnunetv2, acvl-utils, batchgenerators, dynamic-network-architectures\n","  Building wheel for nnunetv2 (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for nnunetv2: filename=nnunetv2-2.2-py3-none-any.whl size=235949 sha256=3305789f0224034da1d18cd86df7e610af52bd5083e3b9be4b3c411e952232e4\n","  Stored in directory: /root/.cache/pip/wheels/9b/b7/20/efb1cae34bb8073bc72f6fbddb84b4cdd3e35af4197c0ca7df\n","  Building wheel for acvl-utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for acvl-utils: filename=acvl_utils-0.2-py3-none-any.whl size=22438 sha256=6e0e4af5b7fc81b9e85f7b2dbfe2921b32f05b0e37f539ebfec00701693597c5\n","  Stored in directory: /root/.cache/pip/wheels/ad/f0/84/52e8897591e66339bd2796681b9540b6c5e453c1461fa92a9e\n","  Building wheel for batchgenerators (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for batchgenerators: filename=batchgenerators-0.25-py3-none-any.whl size=89008 sha256=e6e148af64d6947d5d63f84e9247d07aab94c79ab036bf8ac6461a2bcf34ca8c\n","  Stored in directory: /root/.cache/pip/wheels/9e/b0/1b/40912fb58eb167b86cbc444ddb2e6ba382b248215295f932e2\n","  Building wheel for dynamic-network-architectures (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for dynamic-network-architectures: filename=dynamic_network_architectures-0.2-py3-none-any.whl size=27245 sha256=fabd78612047395a70f98a5cc94d78ec100e4645b565393976d8614668c25421\n","  Stored in directory: /root/.cache/pip/wheels/39/83/85/2ea6c18cc7d707fcd911586e8448ff8a9da1c0274e337f0e49\n","Successfully built nnunetv2 acvl-utils batchgenerators dynamic-network-architectures\n","Installing collected packages: SimpleITK, linecache2, argparse, yacs, traceback2, python-gdcm, pydicom, imagecodecs, connected-components-3d, unittest2, dicom2nifti, batchgenerators, dynamic-network-architectures, acvl-utils, nnunetv2\n","Successfully installed SimpleITK-2.3.0 acvl-utils-0.2 argparse-1.4.0 batchgenerators-0.25 connected-components-3d-3.12.3 dicom2nifti-2.4.8 dynamic-network-architectures-0.2 imagecodecs-2023.9.18 linecache2-1.0.0 nnunetv2-2.2 pydicom-2.4.3 python-gdcm-3.0.22 traceback2-1.4.0 unittest2-1.1.0 yacs-0.1.8\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["argparse"]}}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Current Working Directory /content\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_results exists.\n","If No Error Occured Continue Forward. =)\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia/imagesTr exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia/labelsTr exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia/imagesTs exists.\n","train image files: 5\n","train label files: 5\n","Matches: 0\n","Modality present: msk_007_0000.nii.gz\n","Modality present: msk_011_0000.nii.gz\n","Modality present: msk_021_0000.nii.gz\n","Modality present: msk_007R_0000.nii.gz\n","Modality present: msk_021R_0000.nii.gz\n","Modality present: msk_004_0000.nii.gz\n","dataset.json already exist!\n","dataset.json overwritten!\n","(5, 512, 512, 1) (5, 512, 512, 1)\n","5\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 2000x800 with 10 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\n"},"metadata":{}}],"source":["!pip install nnunetv2\n","import nnunetv2\n","\n","def make_if_dont_exist(folder_path,overwrite=False):\n","    \"\"\"\n","    creates a folder if it does not exists\n","    input:\n","    folder_path : relative path of the folder which needs to be created\n","    over_write :(default: False) if True overwrite the existing folder\n","    \"\"\"\n","    if os.path.exists(folder_path):\n","\n","        if not overwrite:\n","            print(f\"{folder_path} exists.\")\n","        else:\n","            print(f\"{folder_path} overwritten\")\n","            shutil.rmtree(folder_path)\n","            os.makedirs(folder_path)\n","\n","    else:\n","      os.makedirs(folder_path)\n","      print(f\"{folder_path} created!\")\n","\n","# Maybe move path of preprocessed data directly on content - this may be signifcantely faster!\n","print(\"Current Working Directory {}\".format(os.getcwd()))\n","path_dict = {\n","    \"nnUNet_raw\" : os.path.join(mount_dir, \"nnUNet_raw\"),\n","    \"nnUNet_preprocessed\" : os.path.join(mount_dir, \"nnUNet_preprocessed\"), # 1 experiment: 1 epoch took 112s\n","    # \"nnUNet_preprocessed\" : os.path.join(base_dir, \"nnUNet_preprocessed\"), # 1 experiment: 1 epoch took 108s -> seems faster take this\n","    \"nnUNet_results\" : os.path.join(mount_dir, \"nnUNet_results\"),\n","}\n","\n","# Write paths to environment variables\n","for env_var, path in path_dict.items():\n","  os.environ[env_var] = path\n","\n","# Check whether all environment variables are set correct!\n","for env_var, path in path_dict.items():\n","  if os.getenv(env_var) != path:\n","    print(\"Error:\")\n","    print(\"Environment Variable {} is not set correctly!\".format(env_var))\n","    print(\"Should be {}\".format(path))\n","    print(\"Variable is {}\".format(os.getenv(env_var)))\n","  make_if_dont_exist(path, overwrite=False)\n","\n","print(\"If No Error Occured Continue Forward. =)\")\n","\n","\n","\n","\n","\n","\n","# Create Folderstructure for the new task!\n","task_name = 'Dataset001_Tibia' #change here for different task name\n","nnunet_raw = os.getenv(\"nnUNet_raw\")\n","# nnunet_raw_data = \"nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data\"\n","task_folder_name = os.path.join(nnunet_raw,task_name)\n","train_image_dir = os.path.join(task_folder_name,'imagesTr')\n","train_label_dir = os.path.join(task_folder_name,'labelsTr')\n","test_dir = os.path.join(task_folder_name,'imagesTs')\n","# main_dir = os.path.join(base_dir,'nnUNet/nnunet')\n","\n","# Create Folder Structure for the SCGM Task on the system\n","make_if_dont_exist(task_folder_name)\n","make_if_dont_exist(train_image_dir)\n","make_if_dont_exist(train_label_dir)\n","make_if_dont_exist(test_dir)\n","\n","training_data_name=\"training-data\"\n","test_data_name=\"test-data\"\n","\n","\n","\n","\n","train_files = os.listdir(train_image_dir)\n","label_files = os.listdir(train_label_dir)\n","print(\"train image files:\",len(train_files))\n","print(\"train label files:\",len(label_files))\n","print(\"Matches:\",len(set(train_files).intersection(set(label_files))))\n","#renaming to add the modality for SCGM there is only one modality\n","#images should be added with 0000\n","#can be skipped if modality is already mentioned\n","#re-write for multiple modalities\n","\n","def check_modality(filename):\n","    \"\"\"\n","    check for the existence of modality\n","    return False if modality is not found else True\n","    \"\"\"\n","    end = filename.find('.nii.gz')\n","    modality = filename[end-4:end]\n","    for mod in modality:\n","        if not(ord(mod)>=48 and ord(mod)<=57): #if not in 0 to 9 digits\n","            return False\n","    return True\n","\n","def rename_for_single_modality(directory):\n","\n","    for file in os.listdir(directory):\n","\n","        if check_modality(file)==False:\n","            new_name = file[:file.find('.nii.gz')]+\"_0000.nii.gz\"\n","            os.rename(os.path.join(directory,file),os.path.join(directory,new_name))\n","            print(f\"Renamed to {new_name}\")\n","        else:\n","            print(f\"Modality present: {file}\")\n","\n","rename_for_single_modality(train_image_dir)\n","\n","# again skip test due to non available data\n","rename_for_single_modality(test_dir)\n","\n","\n","\n","\n","\n","\n","\n","overwrite_json_file = True #make it True if you want to overwrite the dataset.json file in Task_folder\n","json_file_exist = False\n","\n","if os.path.exists(os.path.join(task_folder_name,'dataset.json')):\n","    print('dataset.json already exist!')\n","    json_file_exist = True\n","\n","if json_file_exist==False or overwrite_json_file:\n","\n","    json_dict = OrderedDict()\n","    json_dict['name'] = task_name\n","    json_dict['description'] = \"Musculoskeletal Lower Limb Segmentation\"\n","    json_dict['tensorImageSize'] = \"3D\"\n","    json_dict['reference'] = \"see challenge website\"\n","    json_dict['licence'] = \"see challenge website\"\n","    json_dict['release'] = \"0.0\"\n","\n","    #you may mention more than one modality\n","    json_dict['channel_names'] = {\n","        \"0\": \"MRI\"\n","    }\n","    #labels+1 should be mentioned for all the labels in the dataset\n","    json_dict['labels'] = {\n","        \"background\": 0,\n","        \"TIBIA\": 1,\n","        \"FEMUR\": 2,\n","        \"FIBULA\": 3,\n","        \"PELVIS\" : 4\n","    }\n","\n","    train_ids = os.listdir(train_label_dir)\n","    test_ids = os.listdir(test_dir)\n","    json_dict['numTraining'] = len(train_ids)\n","    json_dict['numTest'] = len(test_ids)\n","    json_dict['file_ending'] = \".nii.gz\"\n","\n","    #no modality in train image and labels in dataset.json\n","    json_dict['training'] = [{'image': \"./imagesTr/%s\" % i, \"label\": \"./labelsTr/%s\" % i} for i in train_ids]\n","\n","    #removing the modality from test image name to be saved in dataset.json\n","    json_dict['test'] = [\"./imagesTs/%s\" % (i[:i.find(\"_0000\")]+'.nii.gz') for i in test_ids]\n","\n","    with open(os.path.join(task_folder_name,\"dataset.json\"), 'w') as f:\n","        json.dump(json_dict, f, indent=4, sort_keys=True)\n","\n","    if os.path.exists(os.path.join(task_folder_name,'dataset.json')):\n","        if json_file_exist==False:\n","            print('dataset.json created!')\n","        else:\n","            print('dataset.json overwritten!')\n","\n","\n","\n","\n","\n","train_img_name = os.listdir(train_image_dir)[1]\n","train_img = np.array(nib.load(os.path.join(train_image_dir,train_img_name)).dataobj)[80:85,:,:,:]\n","train_label_name = train_img_name[:train_img_name.find('_0000.nii.gz')]+'.nii.gz'\n","train_label = np.array(nib.load(os.path.join(train_label_dir,train_label_name)).dataobj)[80:85,:,:,:]\n","\n","print(train_img.shape,train_label.shape)\n","\n","max_rows = 2\n","max_cols = train_img.shape[0]\n","print(max_cols)\n","\n","fig, axes = plt.subplots(nrows=max_rows, ncols=max_cols, figsize=(20,8))\n","for idx in range(max_cols):\n","    axes[0, idx].axis(\"off\")\n","    axes[0, idx].set_title('Train Image'+str(idx+1))\n","    axes[0, idx].imshow(train_img[idx,:,:,0], cmap=\"gray\")\n","for idx in range(max_cols):\n","    axes[1, idx].axis(\"off\")\n","    axes[1, idx].set_title('Train Label'+str(idx+1))\n","    axes[1, idx].imshow(train_label[idx,:,:,0])\n","\n","plt.subplots_adjust(wspace=.1, hspace=.1)\n","plt.show()"]},{"cell_type":"markdown","source":["Preprocessing"],"metadata":{"id":"BCDAnp2eUvxB"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"44FbDb7CMPMj","outputId":"5d73a93b-fff6-4ea8-a266-c00e72c4a09f","executionInfo":{"status":"ok","timestamp":1695129352499,"user_tz":-720,"elapsed":224298,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Fingerprint extraction...\n","Dataset001_Tibia\n","Using <class 'nnunetv2.imageio.simpleitk_reader_writer.SimpleITKIO'> as reader/writer\n","\n","####################\n","verify_dataset_integrity Done. \n","If you didn't see any error messages then your dataset is most likely OK!\n","####################\n","\n","Using <class 'nnunetv2.imageio.simpleitk_reader_writer.SimpleITKIO'> as reader/writer\n","100% 5/5 [00:20<00:00,  4.16s/it]\n","Experiment planning...\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.03 1.03 1.03]. \n","Current patch size: [128 112 160]. \n","Current median shape: [497.08737864 396.11650485 542.7184466 ]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.0609 1.0609 1.0609]. \n","Current patch size: [128 112 160]. \n","Current median shape: [482.60910548 384.57913093 526.91111321]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.092727 1.092727 1.092727]. \n","Current patch size: [128 112 160]. \n","Current median shape: [468.55252959 373.37779702 511.56418758]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.12550881 1.12550881 1.12550881]. \n","Current patch size: [128 112 160]. \n","Current median shape: [454.90536853 362.50271555 496.66425978]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.15927407 1.15927407 1.15927407]. \n","Current patch size: [128 112 160]. \n","Current median shape: [441.6556976  351.94438403 482.19831047]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.1940523 1.1940523 1.1940523]. \n","Current patch size: [128 112 160]. \n","Current median shape: [428.79193942 341.69357673 468.15369949]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.22987387 1.22987387 1.22987387]. \n","Current patch size: [128 112 160]. \n","Current median shape: [416.30285381 331.74133663 454.51815484]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.26677008 1.26677008 1.26677008]. \n","Current patch size: [128 112 160]. \n","Current median shape: [404.17752797 322.0789676  441.27976198]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.30477318 1.30477318 1.30477318]. \n","Current patch size: [128 112 160]. \n","Current median shape: [392.40536696 312.6980268  428.42695338]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.34391638 1.34391638 1.34391638]. \n","Current patch size: [128 112 160]. \n","Current median shape: [380.97608443 303.59031728 415.94849843]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.38423387 1.38423387 1.38423387]. \n","Current patch size: [128 112 160]. \n","Current median shape: [369.87969362 294.74788085 403.83349362]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.42576089 1.42576089 1.42576089]. \n","Current patch size: [128 112 160]. \n","Current median shape: [359.10649866 286.16299112 392.07135303]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.46853371 1.46853371 1.46853371]. \n","Current patch size: [128 112 160]. \n","Current median shape: [348.64708608 277.82814672 380.65179906]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.51258972 1.51258972 1.51258972]. \n","Current patch size: [128 112 160]. \n","Current median shape: [338.49231658 269.73606477 369.56485345]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.55796742 1.55796742 1.55796742]. \n","Current patch size: [128 112 160]. \n","Current median shape: [328.63331707 261.87967454 358.80082859]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.60470644 1.60470644 1.60470644]. \n","Current patch size: [128 112 160]. \n","Current median shape: [319.06147288 254.2521112  348.35031902]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.65284763 1.65284763 1.65284763]. \n","Current patch size: [128 112 160]. \n","Current median shape: [309.76842027 246.8467099  338.20419323]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.70243306 1.70243306 1.70243306]. \n","Current patch size: [128 112 160]. \n","Current median shape: [300.7460391  239.65699991 328.35358566]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.75350605 1.75350605 1.75350605]. \n","Current patch size: [128 112 160]. \n","Current median shape: [291.98644573 232.67669894 318.78988899]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.80611123 1.80611123 1.80611123]. \n","Current patch size: [128 112 160]. \n","Current median shape: [283.48198614 225.89970771 309.50474659]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.86029457 1.86029457 1.86029457]. \n","Current patch size: [128 112 160]. \n","Current median shape: [275.22522927 219.32010457 300.49004523]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.91610341 1.91610341 1.91610341]. \n","Current patch size: [128 112 160]. \n","Current median shape: [267.20896045 212.93214036 291.73790799]\n","Attempting to find 3d_lowres config. \n","Current spacing: [1.97358651 1.97358651 1.97358651]. \n","Current patch size: [128 112 160]. \n","Current median shape: [259.4261752  206.73023336 283.24068737]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.03279411 2.03279411 2.03279411]. \n","Current patch size: [128 112 160]. \n","Current median shape: [251.87007301 200.70896443 274.99095861]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.09377793 2.09377793 2.09377793]. \n","Current patch size: [128 112 160]. \n","Current median shape: [244.53405146 194.86307226 266.98151322]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.15659127 2.15659127 2.15659127]. \n","Current patch size: [128 112 160]. \n","Current median shape: [237.41170045 189.18744879 259.20535264]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.22128901 2.22128901 2.22128901]. \n","Current patch size: [128 112 160]. \n","Current median shape: [230.49679655 183.67713475 251.65568217]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.28792768 2.28792768 2.28792768]. \n","Current patch size: [128 112 160]. \n","Current median shape: [223.78329762 178.32731529 244.32590502]\n","Attempting to find 3d_lowres config. \n","Current spacing: [2.35656551 2.35656551 2.35656551]. \n","Current patch size: [128 112 160]. \n","Current median shape: [217.2653375  173.13331582 237.20961653]\n","2D U-Net configuration:\n","{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': array([448, 576]), 'median_image_size_in_voxels': array([408., 559.]), 'spacing': array([1., 1.]), 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': (2, 2, 2, 2, 2, 2, 2), 'n_conv_per_stage_decoder': (2, 2, 2, 2, 2, 2), 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}\n","\n","Using <class 'nnunetv2.imageio.simpleitk_reader_writer.SimpleITKIO'> as reader/writer\n","3D lowres U-Net configuration:\n","{'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': array([128, 112, 160]), 'median_image_size_in_voxels': [217, 173, 237], 'spacing': array([2.35656551, 2.35656551, 2.35656551]), 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': (2, 2, 2, 2, 2, 2), 'n_conv_per_stage_decoder': (2, 2, 2, 2, 2), 'num_pool_per_axis': [5, 4, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}\n","\n","3D fullres U-Net configuration:\n","{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': array([128, 112, 160]), 'median_image_size_in_voxels': array([512., 408., 559.]), 'spacing': array([1., 1., 1.]), 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': (2, 2, 2, 2, 2, 2), 'n_conv_per_stage_decoder': (2, 2, 2, 2, 2), 'num_pool_per_axis': [5, 4, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}\n","\n","Plans were saved to /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Dataset001_Tibia/nnUNetPlans.json\n","Preprocessing...\n","Preprocessing dataset Dataset001_Tibia\n","Configuration: 2d...\n","100% 5/5 [00:39<00:00,  7.89s/it]\n","Configuration: 3d_fullres...\n","100% 5/5 [00:54<00:00, 10.89s/it]\n","Configuration: 3d_lowres...\n","100% 5/5 [00:51<00:00, 10.22s/it]\n"]}],"source":["run_Proprocessing = True\n","if (run_Proprocessing == True):\n","  !nnUNetv2_plan_and_preprocess -d 001 --verify_dataset_integrity"]},{"cell_type":"markdown","source":["Training"],"metadata":{"id":"J64mQTesU0jI"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fMdq5sgfcuxv","outputId":"7e934a55-f483-411c-d3ba-fb4e4dc4b0f6","executionInfo":{"status":"ok","timestamp":1695160333444,"user_tz":-720,"elapsed":994792,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cuda:0\n","\n","#######################################################################\n","Please cite the following paper when using nnU-Net:\n","Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.\n","#######################################################################\n","\n","\n","This is the configuration used by this training:\n","Configuration name: 2d\n"," {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [448, 576], 'median_image_size_in_voxels': [408.0, 559.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} \n","\n","These are the global plan.json settings:\n"," {'dataset_name': 'Dataset001_Tibia', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [512, 408, 559], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 0.9786614775657654, 'mean': 0.13927818834781647, 'median': 0.12284913659095764, 'min': 0.0, 'percentile_00_5': 0.0009711880702525377, 'percentile_99_5': 0.572806179523468, 'std': 0.10573729872703552}}} \n","\n","2023-09-19 21:35:42.810467: unpacking dataset...\n","2023-09-19 21:35:46.895712: unpacking done...\n","2023-09-19 21:35:46.898780: do_dummy_2d_data_aug: False\n","2023-09-19 21:35:46.902289: Using splits from existing split file: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Dataset001_Tibia/splits_final.json\n","2023-09-19 21:35:46.905021: The split file contains 5 splits.\n","2023-09-19 21:35:46.906744: Desired fold for training: 4\n","2023-09-19 21:35:46.916957: This split has 4 training and 1 validation cases.\n","2023-09-19 21:35:46.946180: Unable to plot network architecture:\n","2023-09-19 21:35:46.947582: No module named 'hiddenlayer'\n","2023-09-19 21:35:47.850957: \n","2023-09-19 21:35:47.853077: Epoch 0\n","2023-09-19 21:35:47.854792: Current learning rate: 0.01\n","using pin_memory on device 0\n","using pin_memory on device 0\n","2023-09-19 21:38:34.614546: train_loss 0.1174\n","2023-09-19 21:38:34.617165: val_loss -0.0376\n","2023-09-19 21:38:34.618902: Pseudo dice [0.0, 0.0, 0.0, 0.0]\n","2023-09-19 21:38:34.620388: Epoch time: 166.77 s\n","2023-09-19 21:38:34.621906: Yayy! New best EMA pseudo Dice: 0.0\n","2023-09-19 21:38:37.040490: \n","2023-09-19 21:38:37.042280: Epoch 1\n","2023-09-19 21:38:37.043765: Current learning rate: 0.00999\n","2023-09-19 21:41:11.025516: train_loss -0.1539\n","2023-09-19 21:41:11.028462: val_loss -0.1838\n","2023-09-19 21:41:11.030452: Pseudo dice [0.0, 0.4863, 0.0, 0.0]\n","2023-09-19 21:41:11.032198: Epoch time: 153.99 s\n","2023-09-19 21:41:11.033841: Yayy! New best EMA pseudo Dice: 0.0122\n","2023-09-19 21:41:12.997612: \n","2023-09-19 21:41:12.999530: Epoch 2\n","2023-09-19 21:41:13.001457: Current learning rate: 0.00998\n","2023-09-19 21:43:46.688339: train_loss -0.3222\n","2023-09-19 21:43:46.691038: val_loss -0.2902\n","2023-09-19 21:43:46.692850: Pseudo dice [0.0, 0.5363, 0.0, 0.0741]\n","2023-09-19 21:43:46.694368: Epoch time: 153.69 s\n","2023-09-19 21:43:46.695808: Yayy! New best EMA pseudo Dice: 0.0262\n","2023-09-19 21:43:48.742505: \n","2023-09-19 21:43:48.744493: Epoch 3\n","2023-09-19 21:43:48.746228: Current learning rate: 0.00997\n","2023-09-19 21:46:22.667768: train_loss -0.4737\n","2023-09-19 21:46:22.670774: val_loss -0.4174\n","2023-09-19 21:46:22.673177: Pseudo dice [0.6654, 0.6333, 0.0, 0.6567]\n","2023-09-19 21:46:22.675015: Epoch time: 153.93 s\n","2023-09-19 21:46:22.685169: Yayy! New best EMA pseudo Dice: 0.0725\n","2023-09-19 21:46:24.697741: \n","2023-09-19 21:46:24.699551: Epoch 4\n","2023-09-19 21:46:24.701000: Current learning rate: 0.00996\n","2023-09-19 21:48:58.279714: train_loss -0.6006\n","2023-09-19 21:48:58.282990: val_loss -0.4658\n","2023-09-19 21:48:58.285208: Pseudo dice [0.6954, 0.7407, 0.0, 0.6162]\n","2023-09-19 21:48:58.287316: Epoch time: 153.58 s\n","2023-09-19 21:48:58.289709: Yayy! New best EMA pseudo Dice: 0.1165\n","2023-09-19 21:49:00.424553: \n","2023-09-19 21:49:00.426955: Epoch 5\n","2023-09-19 21:49:00.429142: Current learning rate: 0.00995\n","2023-09-19 21:51:34.013648: train_loss -0.6413\n","2023-09-19 21:51:34.016578: val_loss -0.5167\n","2023-09-19 21:51:34.018369: Pseudo dice [0.7914, 0.7552, 0.0, 0.6405]\n","2023-09-19 21:51:34.020046: Epoch time: 153.59 s\n","2023-09-19 21:51:34.021720: Yayy! New best EMA pseudo Dice: 0.1596\n","2023-09-19 21:51:35.935500: \n","2023-09-19 21:51:35.937289: Epoch 6\n","2023-09-19 21:51:35.939129: Current learning rate: 0.00995\n","Traceback (most recent call last):\n","  File \"/usr/local/bin/nnUNetv2_train\", line 8, in <module>\n","    sys.exit(run_training_entry())\n","  File \"/usr/local/lib/python3.10/dist-packages/nnunetv2/run/run_training.py\", line 268, in run_training_entry\n","    run_training(args.dataset_name_or_id, args.configuration, args.fold, args.tr, args.p, args.pretrained_weights,\n","  File \"/usr/local/lib/python3.10/dist-packages/nnunetv2/run/run_training.py\", line 204, in run_training\n","    nnunet_trainer.run_training()\n","  File \"/usr/local/lib/python3.10/dist-packages/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py\", line 1240, in run_training\n","    train_outputs.append(self.train_step(next(self.dataloader_train)))\n","  File \"/usr/local/lib/python3.10/dist-packages/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py\", line 888, in train_step\n","    torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\n","  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/clip_grad.py\", line 76, in clip_grad_norm_\n","    torch._foreach_mul_(grads, clip_coef_clamped.to(device))  # type: ignore[call-overload]\n","KeyboardInterrupt\n","^C\n"]}],"source":["# 3d_fullres, 3d_lowres , 2d, 3d_cascade_fullres\n","run_ModelTraining = True\n","if (run_ModelTraining == True):\n","  !nnUNetv2_train 001 3d_fullres 4\n"]},{"cell_type":"markdown","source":["Postprocessing"],"metadata":{"id":"Wt0dsjtKU8n-"}},{"cell_type":"code","source":["!nnUNetv2_determine_postprocessing -i '/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/nnUNetTrainer__nnUNetPlans__2d' -ref '/content/drive/MyDrive/Colab Notebooks/nnUNet_preprocessed/Dataset001_Tibia/nnUNetPlans_2d'\n","# !nnUNetv2_determine_postprocessing -h\n","# !nnUNetv2_determine_postprocessing -t 120 -tr nnUNetTrainerV2 -p nnUNetTrainer__nnUNetPlans__2d -m 2d"],"metadata":{"id":"5DtbW_QvU7mi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Model Configurations"],"metadata":{"id":"iSsXkokDVCLH"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"h0B94Kw9X2Ow"},"outputs":[],"source":["!nnUNetv2_find_best_configuration 001 -c 2d"]},{"cell_type":"markdown","source":["Prediction/Inference"],"metadata":{"id":"Cd7IjITRVF5-"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zn_HuFu3YqAT","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6758cc39-007a-4d56-ab29-900b2c986ef1","executionInfo":{"status":"ok","timestamp":1695160622183,"user_tz":-720,"elapsed":228917,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","#######################################################################\n","Please cite the following paper when using nnU-Net:\n","Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.\n","#######################################################################\n","\n","There are 1 cases in the source folder\n","I am process 0 out of 1 (max process ID is 0, we start counting with 0!)\n","There are 1 cases that I would like to predict\n","\n","Predicting msk_004:\n","perform_everything_on_gpu: True\n","100% 511/511 [00:35<00:00, 14.58it/s]\n","100% 511/511 [00:35<00:00, 14.24it/s]\n","100% 511/511 [00:35<00:00, 14.45it/s]\n","100% 511/511 [00:35<00:00, 14.37it/s]\n","100% 511/511 [00:35<00:00, 14.37it/s]\n","Prediction done, transferring to CPU if needed\n","sending off prediction to background worker for resampling and export\n","done with msk_004\n"]}],"source":["!nnUNetv2_predict -i '/content/drive/MyDrive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia/imagesTs' -o '/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs' -d 001 -c 2d"]},{"cell_type":"markdown","source":["2D Visualisation of Segmentation Results"],"metadata":{"id":"qI_o85aXVOrk"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":891},"executionInfo":{"elapsed":13423,"status":"ok","timestamp":1694327089685,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"E14Gjhvq7HlC","outputId":"c5adb946-e9d8-4702-9b48-1cf12fb5c497"},"outputs":[{"output_type":"stream","name":"stdout","text":["Prediction Segmentation Shape:  (534, 512, 512, 1)\n","Raw Scan Shape:  (534, 512, 512, 1)\n","Groundtruth Segmentation Shape:  (534, 512, 512, 1)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["def superimpose_images(image1, image2):\n","    image1 = image1 / np.max(image1)\n","    image2 = image2 / np.max(image2)\n","    alpha = 0.5\n","    superimposed_image = alpha * image1 + (1 - alpha) * image2\n","    return superimposed_image\n","\n","slice_idx = 50\n","\n","pred_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/msk_018.nii.gz')\n","pred_img = nib.load(pred_path)\n","pred_img_data = pred_img.get_fdata()\n","pred_img_data = np.expand_dims(pred_img_data, axis=-1)\n","print(\"Prediction Segmentation Shape: \", pred_img_data.shape)\n","\n","raw_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_raw/Dataset001_Tibia/imagesTs/msk_018_0000.nii.gz')\n","raw_img = nib.load(raw_path)\n","raw_img_data = raw_img.get_fdata()\n","# raw_img_data = np.expand_dims(raw_img_data, axis=-1)\n","print(\"Raw Scan Shape: \", raw_img_data.shape)\n","\n","gt_path = '/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/Groundtruth Segmentations/msk_018.nii.gz'\n","gt_img = nib.load(gt_path)\n","gt_img_data = gt_img.get_fdata()\n","print(\"Groundtruth Segmentation Shape: \", gt_img_data.shape)\n","\n","image1 = raw_img_data[slice_idx, :, :, :]\n","image2 = pred_img_data[slice_idx, :, :, :]\n","superimposed_image = superimpose_images(image1, image2)\n","plt.imshow(superimposed_image, cmap='gray')\n","plt.axis('off')\n","plt.title(('Automatic Segmentation Result on Slice {}').format(slice_idx))\n","plt.show()\n","\n","image1 = raw_img_data[slice_idx, :, :, :]\n","image2 = gt_img_data[slice_idx, :, :, :]\n","superimposed_image = superimpose_images(image1, image2)\n","plt.imshow(superimposed_image, cmap='gray')\n","plt.axis('off')\n","plt.title(('Manual Segmentation Result on Slice {}').format(slice_idx))\n","plt.show()\n"]},{"cell_type":"markdown","source":["3D Visualisation of Segmentation Results"],"metadata":{"id":"Diu3wr8XVWnj"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20913,"status":"ok","timestamp":1694327158932,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"FkR_5KP9rOdW","outputId":"7fe8ecbc-8548-44b1-ac5f-b6a7fc13934f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting trimesh\n","  Downloading trimesh-3.23.5-py3-none-any.whl (685 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m685.4/685.4 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from trimesh) (1.23.5)\n","Installing collected packages: trimesh\n","Successfully installed trimesh-3.23.5\n"]}],"source":["import SimpleITK as sitk\n","import numpy as np\n","from skimage.measure import marching_cubes\n","%pip install trimesh\n","import trimesh\n","\n","predfname = 'msk_018'\n","model_used = '2D-UNet'\n","\n","def Export3DStructure(segmentation_data, predfname, class_msk, model_used):\n","    # Generate a surface mesh using marching cubes\n","    vertices, faces, normals, _ = marching_cubes(segmentation_data, level=0)\n","\n","    # Create a Trimesh object\n","    mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_normals=normals)\n","\n","\n","    # Save the mesh as a PLY file\n","    ply_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/{}_{}_{}.ply').format(predfname, class_msk, model_used)\n","    mesh.export(ply_path)\n","\n","# Load the NIfTI file\n","nifti_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/{}.nii.gz').format(predfname)\n","nifti_img = sitk.ReadImage(nifti_path)\n","segmentation_data_all = sitk.GetArrayFromImage(nifti_img)\n","segmentation_data_all = np.flip(segmentation_data_all)\n","segmentation_data_all = np.rot90(segmentation_data_all, k=2)\n","\n","tibia_seg_data = np.where(segmentation_data_all != 1, 0, 1)\n","femur_seg_data = np.where(segmentation_data_all != 2, 0, 2)\n","fibula_seg_data = np.where(segmentation_data_all != 3, 0, 3)\n","pelvis_seg_data = np.where(segmentation_data_all != 4, 0, 4)\n","\n","# segmentation_data = [segmentation_data_all, tibia_seg_data, femur_seg_data, fibula_seg_data, pelvis_seg_data]\n","# class_msk = ['ALL', 'TIBIA', 'FEMUR', 'FIBULA', 'PELVIS']\n","\n","segmentation_data = [segmentation_data_all, tibia_seg_data, femur_seg_data, fibula_seg_data]\n","class_msk = ['ALL', 'TIBIA', 'FEMUR', 'FIBULA']\n","# Specify colors (RGBA format) for each class_msk\n","colors = [(255, 0, 0, 255),    # Red\n","          (0, 255, 0, 255),    # Green\n","          (0, 0, 255, 255),    # Blue\n","          (255, 255, 0, 255)]  # Yellow\n","\n","for i in range (len(segmentation_data)):\n","  Export3DStructure(segmentation_data[i], predfname, class_msk[i], model_used)"]},{"cell_type":"markdown","source":["Performance Evaluation"],"metadata":{"id":"ySIM-P5dVY59"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":750},"executionInfo":{"elapsed":11437,"status":"ok","timestamp":1695161057737,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"jjcojQJwrhYH","outputId":"d3b00a5c-531a-4d9b-ce97-10fd0dde3373"},"outputs":[{"output_type":"stream","name":"stdout","text":["Prediction Segmentation Shape:  (542, 512, 512, 1)\n","Groundtruth Segmentation Shape:  (542, 512, 512, 1)\n","\n","\n","Dice Similarity Coefficient (DSC) Metric Values ---> TIBIA: 0.77, FEMUR: 0.86, FIBULA: 0.0, PELVIS: 0.64\n","\n","\n","Volume Error (VError) Metric Value ---> TIBIA: 0.31, FEMUR: 0.14, FIBULA: 1.0, PELVIS: 0.03\n","\n","\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-8-651cf3f5075d>:27: RuntimeWarning: invalid value encountered in divide\n","  image1 = image1 / np.max(image1)\n","<ipython-input-8-651cf3f5075d>:28: RuntimeWarning: invalid value encountered in divide\n","  image2 = image2 / np.max(image2)\n","<ipython-input-8-651cf3f5075d>:79: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n","  cmap_superimposed = plt.cm.get_cmap(cmap_binary)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from scipy.spatial.distance import directed_hausdorff\n","\n","\n","model = '2D UNet'\n","patient_fname = 'msk_004'\n","# Is the mask post processed?\n","post_processed = False\n","\n","def volume_error(y_true, y_pred):\n","    # mm3\n","    Voxel_Volume = 2.63671875\n","\n","    N_true = np.count_nonzero(y_true)\n","    N_pred = np.count_nonzero(y_pred)\n","\n","    volume_error = np.abs(N_true - N_pred) * Voxel_Volume\n","\n","    return round(volume_error * 0.001, 2)\n","\n","\n","def dice_coefficient(true_array, pred_array):\n","    true_array = np.asarray(true_array).astype(bool)\n","    pred_array = np.asarray(pred_array).astype(bool)\n","\n","    intersection = np.logical_and(true_array, pred_array)\n","    dice = 2.0 * intersection.sum() / (true_array.sum() + pred_array.sum())\n","\n","    return round(dice, 2)\n","\n","\n","def superimpose_images(image1, image2):\n","    image1 = image1 / np.max(image1)\n","    image2 = image2 / np.max(image2)\n","    alpha = 0.5\n","    superimposed_image = alpha * image1 + (1 - alpha) * image2\n","    return superimposed_image\n","\n","pred_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/{}.nii.gz').format(patient_fname)\n","gt_path = ('/content/drive/MyDrive/Colab Notebooks/nnUNet_results/Dataset001_Tibia/predTs/Groundtruth Segmentations/{}.nii.gz').format(patient_fname)\n","pred_img = nib.load(pred_path)\n","gt_img = nib.load(gt_path)\n","pred_img_data = pred_img.get_fdata()\n","if (post_processed == False):\n","  pred_img_data = np.expand_dims(pred_img_data, axis=-1)\n","gt_img_data = gt_img.get_fdata()\n","\n","tibia_seg_data_pred = np.where(pred_img_data != 1, 0, 1)\n","femur_seg_data_pred = np.where(pred_img_data != 2, 0, 1)\n","fibula_seg_data_pred = np.where(pred_img_data != 3, 0, 1)\n","pelvis_seg_data_pred = np.where(pred_img_data != 4, 0, 1)\n","\n","tibia_seg_data_gt = np.where(gt_img_data != 1, 0, 1)\n","femur_seg_data_gt = np.where(gt_img_data != 2, 0, 1)\n","fibula_seg_data_gt = np.where(gt_img_data != 3, 0, 1)\n","pelvis_seg_data_gt = np.where(gt_img_data != 4, 0, 1)\n","\n","# np.savetxt('output.txt', combined_mask[450,:,:,0], fmt=\"%d\", delimiter=\",\")\n","print(\"Prediction Segmentation Shape: \", pred_img_data.shape)\n","print(\"Groundtruth Segmentation Shape: \", gt_img_data.shape)\n","print('\\n')\n","\n","DSC_TIBIA = dice_coefficient(tibia_seg_data_gt, tibia_seg_data_pred)\n","DSC_FEMUR = dice_coefficient(femur_seg_data_gt, femur_seg_data_pred)\n","DSC_FIBULA = dice_coefficient(fibula_seg_data_gt, fibula_seg_data_pred)\n","DSC_PELVIS = dice_coefficient(pelvis_seg_data_gt, pelvis_seg_data_pred)\n","print(('Dice Similarity Coefficient (DSC) Metric Values ---> TIBIA: {}, FEMUR: {}, FIBULA: {}, PELVIS: {}').format(DSC_TIBIA, DSC_FEMUR, DSC_FIBULA, DSC_PELVIS))\n","print('\\n')\n","\n","TIBIA_VError = volume_error(tibia_seg_data_gt, tibia_seg_data_pred)\n","FEMUR_VError = volume_error(femur_seg_data_gt, femur_seg_data_pred)\n","FIBULA_VError = volume_error(fibula_seg_data_gt, fibula_seg_data_pred)\n","PELVIS_VError = volume_error(pelvis_seg_data_gt, pelvis_seg_data_pred)\n","print(('Volume Error (VError) Metric Value ---> TIBIA: {}, FEMUR: {}, FIBULA: {}, PELVIS: {}').format(TIBIA_VError, FEMUR_VError, FIBULA_VError, PELVIS_VError))\n","print('\\n')\n","\n","slice = 200\n","cmap_binary = 'YlOrBr'\n","superimposed_image = superimpose_images(tibia_seg_data_gt[slice, :, :, :], tibia_seg_data_pred[slice, :, :, :])\n","# Define the cropping ranges\n","x_start, x_end = 200, 400\n","y_start, y_end = 200, 400\n","cropped_image = superimposed_image[y_start:y_end, x_start:x_end]\n","fig, ax = plt.subplots()\n","cmap_superimposed = plt.cm.get_cmap(cmap_binary)\n","ax.imshow(cropped_image, cmap=cmap_superimposed, vmin=0, vmax=1)\n","ax.axis('on')\n","plt.title(('Superimposed Prediction Mask (Orange) & Groundtruth Mask (Black) on Slice {}').format(slice))\n","plt.show()"]},{"cell_type":"markdown","source":["Check Mask Validity"],"metadata":{"id":"2splIORDVc2J"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":577,"status":"ok","timestamp":1692676279812,"user":{"displayName":"Asif Cheena","userId":"14143847646622962473"},"user_tz":-720},"id":"sav-DOlZXE61","outputId":"647f277b-2bac-4525-9111-8eb4e9efee8f"},"outputs":[{"name":"stdout","output_type":"stream","text":["True\n","False\n"]}],"source":["def is_binarized_array(arr):\n","    if isinstance(arr, np.ndarray):\n","        return np.all((arr == 0) | (arr == 1))\n","    else:\n","        for element in arr:\n","            if element != 0 and element != 1:\n","                return False\n","        return True\n","# Example usage\n","binary_array = [0, 1, 0, 1, 1]\n","non_binary_array = [0, 1, 2, 1, 0]\n","\n","print(is_binarized_array(fibula_seg_data_gt))  # Output: True\n","print(is_binarized_array(fibula_seg_data_pred))  # Output: False"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[{"file_id":"16dM7RkPgKYHaGOD0To_tBd252KDggJwm","timestamp":1691545247282}],"gpuType":"T4","authorship_tag":"ABX9TyN7rKVYEBK0FNxMJi/moYlE"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}