nnUNet_calvingfront_detection
/
nnunet
/dataset_conversion
/Task017_BeyondCranialVaultAbdominalOrganSegmentation.py
| # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from collections import OrderedDict | |
| from nnunet.paths import nnUNet_raw_data | |
| from batchgenerators.utilities.file_and_folder_operations import * | |
| import shutil | |
| if __name__ == "__main__": | |
| base = "/media/yunlu/10TB/research/other_data/Multi-Atlas Labeling Beyond the Cranial Vault/RawData/" | |
| task_id = 17 | |
| task_name = "AbdominalOrganSegmentation" | |
| prefix = 'ABD' | |
| foldername = "Task%03.0d_%s" % (task_id, task_name) | |
| out_base = join(nnUNet_raw_data, foldername) | |
| imagestr = join(out_base, "imagesTr") | |
| imagests = join(out_base, "imagesTs") | |
| labelstr = join(out_base, "labelsTr") | |
| maybe_mkdir_p(imagestr) | |
| maybe_mkdir_p(imagests) | |
| maybe_mkdir_p(labelstr) | |
| train_folder = join(base, "Training/img") | |
| label_folder = join(base, "Training/label") | |
| test_folder = join(base, "Test/img") | |
| train_patient_names = [] | |
| test_patient_names = [] | |
| train_patients = subfiles(train_folder, join=False, suffix = 'nii.gz') | |
| for p in train_patients: | |
| serial_number = int(p[3:7]) | |
| train_patient_name = f'{prefix}_{serial_number:03d}.nii.gz' | |
| label_file = join(label_folder, f'label{p[3:]}') | |
| image_file = join(train_folder, p) | |
| shutil.copy(image_file, join(imagestr, f'{train_patient_name[:7]}_0000.nii.gz')) | |
| shutil.copy(label_file, join(labelstr, train_patient_name)) | |
| train_patient_names.append(train_patient_name) | |
| test_patients = subfiles(test_folder, join=False, suffix=".nii.gz") | |
| for p in test_patients: | |
| p = p[:-7] | |
| image_file = join(test_folder, p + ".nii.gz") | |
| serial_number = int(p[3:7]) | |
| test_patient_name = f'{prefix}_{serial_number:03d}.nii.gz' | |
| shutil.copy(image_file, join(imagests, f'{test_patient_name[:7]}_0000.nii.gz')) | |
| test_patient_names.append(test_patient_name) | |
| json_dict = OrderedDict() | |
| json_dict['name'] = "AbdominalOrganSegmentation" | |
| json_dict['description'] = "Multi-Atlas Labeling Beyond the Cranial Vault Abdominal Organ Segmentation" | |
| json_dict['tensorImageSize'] = "3D" | |
| json_dict['reference'] = "https://www.synapse.org/#!Synapse:syn3193805/wiki/217789" | |
| json_dict['licence'] = "see challenge website" | |
| json_dict['release'] = "0.0" | |
| json_dict['modality'] = { | |
| "0": "CT", | |
| } | |
| json_dict['labels'] = OrderedDict({ | |
| "00": "background", | |
| "01": "spleen", | |
| "02": "right kidney", | |
| "03": "left kidney", | |
| "04": "gallbladder", | |
| "05": "esophagus", | |
| "06": "liver", | |
| "07": "stomach", | |
| "08": "aorta", | |
| "09": "inferior vena cava", | |
| "10": "portal vein and splenic vein", | |
| "11": "pancreas", | |
| "12": "right adrenal gland", | |
| "13": "left adrenal gland"} | |
| ) | |
| json_dict['numTraining'] = len(train_patient_names) | |
| json_dict['numTest'] = len(test_patient_names) | |
| json_dict['training'] = [{'image': "./imagesTr/%s" % train_patient_name, "label": "./labelsTr/%s" % train_patient_name} for i, train_patient_name in enumerate(train_patient_names)] | |
| json_dict['test'] = ["./imagesTs/%s" % test_patient_name for test_patient_name in test_patient_names] | |
| save_json(json_dict, os.path.join(out_base, "dataset.json")) | |