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nnUNet_calvingfront_detection / nnunet /dataset_conversion /Task135_KiTS2021.py
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from batchgenerators.utilities.file_and_folder_operations import *
import shutil
from nnunet.paths import nnUNet_raw_data
from nnunet.dataset_conversion.utils import generate_dataset_json
if __name__ == '__main__':
# this is the data folder from the kits21 github repository, see https://github.com/neheller/kits21
kits_data_dir = '/home/fabian/git_repos/kits21/kits21/data'
# This script uses the majority voted segmentation as ground truth
kits_segmentation_filename = 'aggregated_MAJ_seg.nii.gz'
# Arbitrary task id. This is just to ensure each dataset ha a unique number. Set this to whatever ([0-999]) you
# want
task_id = 135
task_name = "KiTS2021"
foldername = "Task%03.0d_%s" % (task_id, task_name)
# setting up nnU-Net folders
out_base = join(nnUNet_raw_data, foldername)
imagestr = join(out_base, "imagesTr")
labelstr = join(out_base, "labelsTr")
maybe_mkdir_p(imagestr)
maybe_mkdir_p(labelstr)
case_ids = subdirs(kits_data_dir, prefix='case_', join=False)
for c in case_ids:
if isfile(join(kits_data_dir, c, kits_segmentation_filename)):
shutil.copy(join(kits_data_dir, c, kits_segmentation_filename), join(labelstr, c + '.nii.gz'))
shutil.copy(join(kits_data_dir, c, 'imaging.nii.gz'), join(imagestr, c + '_0000.nii.gz'))
generate_dataset_json(join(out_base, 'dataset.json'),
imagestr,
None,
('CT',),
{
0: 'background',
1: "kidney",
2: "tumor",
3: "cyst",
},
task_name,
license='see https://kits21.kits-challenge.org/participate#download-block',
dataset_description='see https://kits21.kits-challenge.org/',
dataset_reference='https://www.sciencedirect.com/science/article/abs/pii/S1361841520301857, '
'https://kits21.kits-challenge.org/',
dataset_release='0')