| from batchgenerators.utilities.file_and_folder_operations import * |
| import shutil |
| from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json |
| from nnunetv2.paths import nnUNet_raw |
|
|
|
|
| def convert_amos_task2(amos_base_dir: str, nnunet_dataset_id: int = 219): |
| """ |
| AMOS doesn't say anything about how the validation set is supposed to be used. So we just incorporate that into |
| the train set. Having a 5-fold cross-validation is superior to a single train:val split |
| """ |
| task_name = "AMOS2022_postChallenge_task2" |
|
|
| foldername = "Dataset%03.0d_%s" % (nnunet_dataset_id, task_name) |
|
|
| |
| out_base = join(nnUNet_raw, 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) |
|
|
| dataset_json_source = load_json(join(amos_base_dir, 'dataset.json')) |
|
|
| training_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['training']] |
| for tr in training_identifiers: |
| shutil.copy(join(amos_base_dir, 'imagesTr', tr + '.nii.gz'), join(imagestr, f'{tr}_0000.nii.gz')) |
| shutil.copy(join(amos_base_dir, 'labelsTr', tr + '.nii.gz'), join(labelstr, f'{tr}.nii.gz')) |
|
|
| test_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['test']] |
| for ts in test_identifiers: |
| shutil.copy(join(amos_base_dir, 'imagesTs', ts + '.nii.gz'), join(imagests, f'{ts}_0000.nii.gz')) |
|
|
| val_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['validation']] |
| for vl in val_identifiers: |
| shutil.copy(join(amos_base_dir, 'imagesVa', vl + '.nii.gz'), join(imagestr, f'{vl}_0000.nii.gz')) |
| shutil.copy(join(amos_base_dir, 'labelsVa', vl + '.nii.gz'), join(labelstr, f'{vl}.nii.gz')) |
|
|
| generate_dataset_json(out_base, {0: "either_CT_or_MR"}, labels={v: int(k) for k,v in dataset_json_source['labels'].items()}, |
| num_training_cases=len(training_identifiers) + len(val_identifiers), file_ending='.nii.gz', |
| dataset_name=task_name, reference='https://amos22.grand-challenge.org/', |
| release='https://zenodo.org/record/7262581', |
| overwrite_image_reader_writer='NibabelIOWithReorient', |
| description="This is the dataset as released AFTER the challenge event. It has the " |
| "validation set gt in it! We just use the validation images as additional " |
| "training cases because AMOS doesn't specify how they should be used. nnU-Net's" |
| " 5-fold CV is better than some random train:val split.") |
|
|
|
|
| if __name__ == '__main__': |
| import argparse |
| parser = argparse.ArgumentParser() |
| parser.add_argument('input_folder', type=str, |
| help="The downloaded and extracted AMOS2022 (https://amos22.grand-challenge.org/) data. " |
| "Use this link: https://zenodo.org/record/7262581." |
| "You need to specify the folder with the imagesTr, imagesVal, labelsTr etc subfolders here!") |
| parser.add_argument('-d', required=False, type=int, default=219, help='nnU-Net Dataset ID, default: 219') |
| args = parser.parse_args() |
| amos_base = args.input_folder |
| convert_amos_task2(amos_base, args.d) |
|
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| |
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|
