nnUNet_calvingfront_detection
/
nnunet
/dataset_conversion
/Task035_ISBI_MSLesionSegmentationChallenge.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. | |
| import shutil | |
| from collections import OrderedDict | |
| import numpy as np | |
| import SimpleITK as sitk | |
| import multiprocessing | |
| from batchgenerators.utilities.file_and_folder_operations import * | |
| def convert_to_nii_gz(filename): | |
| f = sitk.ReadImage(filename) | |
| sitk.WriteImage(f, os.path.splitext(filename)[0] + ".nii.gz") | |
| os.remove(filename) | |
| def convert_for_submission(source_dir, target_dir): | |
| files = subfiles(source_dir, suffix=".nii.gz", join=False) | |
| maybe_mkdir_p(target_dir) | |
| for f in files: | |
| splitted = f.split("__") | |
| case_id = int(splitted[1]) | |
| timestep = int(splitted[2][:-7]) | |
| t = join(target_dir, "test%02d_%02d_nnUNet.nii" % (case_id, timestep)) | |
| img = sitk.ReadImage(join(source_dir, f)) | |
| sitk.WriteImage(img, t) | |
| if __name__ == "__main__": | |
| # convert to nifti.gz | |
| dirs = ['/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/imagesTr', | |
| '/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/imagesTs', | |
| '/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/labelsTr'] | |
| p = multiprocessing.Pool(3) | |
| for d in dirs: | |
| nii_files = subfiles(d, suffix='.nii') | |
| p.map(convert_to_nii_gz, nii_files) | |
| p.close() | |
| p.join() | |
| def rename_files(folder): | |
| all_files = subfiles(folder, join=False) | |
| # there are max 14 patients per folder, starting with 1 | |
| for patientid in range(1, 15): | |
| # there are certainly no more than 10 time steps per patient, starting with 1 | |
| for t in range(1, 10): | |
| patient_files = [i for i in all_files if i.find("%02.0d_%02.0d_" % (patientid, t)) != -1] | |
| if not len(patient_files) == 4: | |
| continue | |
| flair_file = [i for i in patient_files if i.endswith("_flair_pp.nii.gz")][0] | |
| mprage_file = [i for i in patient_files if i.endswith("_mprage_pp.nii.gz")][0] | |
| pd_file = [i for i in patient_files if i.endswith("_pd_pp.nii.gz")][0] | |
| t2_file = [i for i in patient_files if i.endswith("_t2_pp.nii.gz")][0] | |
| os.rename(join(folder, flair_file), join(folder, "case__%02.0d__%02.0d_0000.nii.gz" % (patientid, t))) | |
| os.rename(join(folder, mprage_file), join(folder, "case__%02.0d__%02.0d_0001.nii.gz" % (patientid, t))) | |
| os.rename(join(folder, pd_file), join(folder, "case__%02.0d__%02.0d_0002.nii.gz" % (patientid, t))) | |
| os.rename(join(folder, t2_file), join(folder, "case__%02.0d__%02.0d_0003.nii.gz" % (patientid, t))) | |
| for d in dirs[:-1]: | |
| rename_files(d) | |
| # now we have to deal with the training masks, we do it the quick and dirty way here by just creating copies of the | |
| # training data | |
| train_folder = '/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/imagesTr' | |
| for patientid in range(1, 6): | |
| for t in range(1, 6): | |
| fnames_original = subfiles(train_folder, prefix="case__%02.0d__%02.0d" % (patientid, t), suffix=".nii.gz", sort=True) | |
| for f in fnames_original: | |
| for mask in [1, 2]: | |
| fname_target = f[:-12] + "__mask%d" % mask + f[-12:] | |
| shutil.copy(f, fname_target) | |
| os.remove(f) | |
| labels_folder = '/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/labelsTr' | |
| for patientid in range(1, 6): | |
| for t in range(1, 6): | |
| for mask in [1, 2]: | |
| f = join(labels_folder, "training%02d_%02d_mask%d.nii.gz" % (patientid, t, mask)) | |
| if isfile(f): | |
| os.rename(f, join(labels_folder, "case__%02.0d__%02.0d__mask%d.nii.gz" % (patientid, t, mask))) | |
| tr_files = [] | |
| for patientid in range(1, 6): | |
| for t in range(1, 6): | |
| for mask in [1, 2]: | |
| if isfile(join(labels_folder, "case__%02.0d__%02.0d__mask%d.nii.gz" % (patientid, t, mask))): | |
| tr_files.append("case__%02.0d__%02.0d__mask%d.nii.gz" % (patientid, t, mask)) | |
| ts_files = [] | |
| for patientid in range(1, 20): | |
| for t in range(1, 20): | |
| if isfile(join("/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/imagesTs", | |
| "case__%02.0d__%02.0d_0000.nii.gz" % (patientid, t))): | |
| ts_files.append("case__%02.0d__%02.0d.nii.gz" % (patientid, t)) | |
| out_base = '/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/' | |
| json_dict = OrderedDict() | |
| json_dict['name'] = "ISBI_Lesion_Segmentation_Challenge_2015" | |
| json_dict['description'] = "nothing" | |
| json_dict['tensorImageSize'] = "4D" | |
| json_dict['reference'] = "see challenge website" | |
| json_dict['licence'] = "see challenge website" | |
| json_dict['release'] = "0.0" | |
| json_dict['modality'] = { | |
| "0": "flair", | |
| "1": "mprage", | |
| "2": "pd", | |
| "3": "t2" | |
| } | |
| json_dict['labels'] = { | |
| "0": "background", | |
| "1": "lesion" | |
| } | |
| json_dict['numTraining'] = len(subfiles(labels_folder)) | |
| json_dict['numTest'] = len(subfiles('/media/fabian/My Book/MedicalDecathlon/Task035_ISBILesionSegmentation/imagesTs')) // 4 | |
| json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i[:-7], "label": "./labelsTr/%s.nii.gz" % i[:-7]} for i in | |
| tr_files] | |
| json_dict['test'] = ["./imagesTs/%s.nii.gz" % i[:-7] for i in ts_files] | |
| save_json(json_dict, join(out_base, "dataset.json")) | |
| case_identifiers = np.unique([i[:-12] for i in subfiles("/media/fabian/My Book/MedicalDecathlon/MedicalDecathlon_raw_splitted/Task035_ISBILesionSegmentation/imagesTr", suffix='.nii.gz', join=False)]) | |
| splits = [] | |
| for f in range(5): | |
| cases = [i for i in range(1, 6) if i != f+1] | |
| splits.append(OrderedDict()) | |
| splits[-1]['val'] = np.array([i for i in case_identifiers if i.startswith("case__%02d__" % (f + 1))]) | |
| remaining = [i for i in case_identifiers if i not in splits[-1]['val']] | |
| splits[-1]['train'] = np.array(remaining) | |
| maybe_mkdir_p("/media/fabian/nnunet/Task035_ISBILesionSegmentation") | |
| save_pickle(splits, join("/media/fabian/nnunet/Task035_ISBILesionSegmentation", "splits_final.pkl")) | |