nnUNet_calvingfront_detection
/
nnunet
/dataset_conversion
/Task027_AutomaticCardiacDetectionChallenge.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 batchgenerators.utilities.file_and_folder_operations import * | |
| import shutil | |
| import numpy as np | |
| from sklearn.model_selection import KFold | |
| def convert_to_submission(source_dir, target_dir): | |
| niftis = subfiles(source_dir, join=False, suffix=".nii.gz") | |
| patientids = np.unique([i[:10] for i in niftis]) | |
| maybe_mkdir_p(target_dir) | |
| for p in patientids: | |
| files_of_that_patient = subfiles(source_dir, prefix=p, suffix=".nii.gz", join=False) | |
| assert len(files_of_that_patient) | |
| files_of_that_patient.sort() | |
| # first is ED, second is ES | |
| shutil.copy(join(source_dir, files_of_that_patient[0]), join(target_dir, p + "_ED.nii.gz")) | |
| shutil.copy(join(source_dir, files_of_that_patient[1]), join(target_dir, p + "_ES.nii.gz")) | |
| if __name__ == "__main__": | |
| folder = "/media/fabian/My Book/datasets/ACDC/training" | |
| folder_test = "/media/fabian/My Book/datasets/ACDC/testing/testing" | |
| out_folder = "/media/fabian/My Book/MedicalDecathlon/MedicalDecathlon_raw_splitted/Task027_ACDC" | |
| maybe_mkdir_p(join(out_folder, "imagesTr")) | |
| maybe_mkdir_p(join(out_folder, "imagesTs")) | |
| maybe_mkdir_p(join(out_folder, "labelsTr")) | |
| # train | |
| all_train_files = [] | |
| patient_dirs_train = subfolders(folder, prefix="patient") | |
| for p in patient_dirs_train: | |
| current_dir = p | |
| data_files_train = [i for i in subfiles(current_dir, suffix=".nii.gz") if i.find("_gt") == -1 and i.find("_4d") == -1] | |
| corresponding_seg_files = [i[:-7] + "_gt.nii.gz" for i in data_files_train] | |
| for d, s in zip(data_files_train, corresponding_seg_files): | |
| patient_identifier = d.split("/")[-1][:-7] | |
| all_train_files.append(patient_identifier + "_0000.nii.gz") | |
| shutil.copy(d, join(out_folder, "imagesTr", patient_identifier + "_0000.nii.gz")) | |
| shutil.copy(s, join(out_folder, "labelsTr", patient_identifier + ".nii.gz")) | |
| # test | |
| all_test_files = [] | |
| patient_dirs_test = subfolders(folder_test, prefix="patient") | |
| for p in patient_dirs_test: | |
| current_dir = p | |
| data_files_test = [i for i in subfiles(current_dir, suffix=".nii.gz") if i.find("_gt") == -1 and i.find("_4d") == -1] | |
| for d in data_files_test: | |
| patient_identifier = d.split("/")[-1][:-7] | |
| all_test_files.append(patient_identifier + "_0000.nii.gz") | |
| shutil.copy(d, join(out_folder, "imagesTs", patient_identifier + "_0000.nii.gz")) | |
| json_dict = OrderedDict() | |
| json_dict['name'] = "ACDC" | |
| json_dict['description'] = "cardias cine MRI segmentation" | |
| json_dict['tensorImageSize'] = "4D" | |
| json_dict['reference'] = "see ACDC challenge" | |
| json_dict['licence'] = "see ACDC challenge" | |
| json_dict['release'] = "0.0" | |
| json_dict['modality'] = { | |
| "0": "MRI", | |
| } | |
| json_dict['labels'] = { | |
| "0": "background", | |
| "1": "RV", | |
| "2": "MLV", | |
| "3": "LVC" | |
| } | |
| json_dict['numTraining'] = len(all_train_files) | |
| json_dict['numTest'] = len(all_test_files) | |
| json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1][:-12], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1][:-12]} for i in | |
| all_train_files] | |
| json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1][:-12] for i in all_test_files] | |
| save_json(json_dict, os.path.join(out_folder, "dataset.json")) | |
| # create a dummy split (patients need to be separated) | |
| splits = [] | |
| patients = np.unique([i[:10] for i in all_train_files]) | |
| patientids = [i[:-12] for i in all_train_files] | |
| kf = KFold(5, True, 12345) | |
| for tr, val in kf.split(patients): | |
| splits.append(OrderedDict()) | |
| tr_patients = patients[tr] | |
| splits[-1]['train'] = [i[:-12] for i in all_train_files if i[:10] in tr_patients] | |
| val_patients = patients[val] | |
| splits[-1]['val'] = [i[:-12] for i in all_train_files if i[:10] in val_patients] | |
| save_pickle(splits, "/media/fabian/nnunet/Task027_ACDC/splits_final.pkl") |