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nnUNet_calvingfront_detection / nnunet /dataset_conversion /Task024_Promise2012.py
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# 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
import SimpleITK as sitk
from batchgenerators.utilities.file_and_folder_operations import *
def export_for_submission(source_dir, target_dir):
"""
promise wants mhd :-/
:param source_dir:
:param target_dir:
:return:
"""
files = subfiles(source_dir, suffix=".nii.gz", join=False)
target_files = [join(target_dir, i[:-7] + ".mhd") for i in files]
maybe_mkdir_p(target_dir)
for f, t in zip(files, target_files):
img = sitk.ReadImage(join(source_dir, f))
sitk.WriteImage(img, t)
if __name__ == "__main__":
folder = "/media/fabian/My Book/datasets/promise2012"
out_folder = "/media/fabian/My Book/MedicalDecathlon/MedicalDecathlon_raw_splitted/Task024_Promise"
maybe_mkdir_p(join(out_folder, "imagesTr"))
maybe_mkdir_p(join(out_folder, "imagesTs"))
maybe_mkdir_p(join(out_folder, "labelsTr"))
# train
current_dir = join(folder, "train")
segmentations = subfiles(current_dir, suffix="segmentation.mhd")
raw_data = [i for i in subfiles(current_dir, suffix="mhd") if not i.endswith("segmentation.mhd")]
for i in raw_data:
out_fname = join(out_folder, "imagesTr", i.split("/")[-1][:-4] + "_0000.nii.gz")
sitk.WriteImage(sitk.ReadImage(i), out_fname)
for i in segmentations:
out_fname = join(out_folder, "labelsTr", i.split("/")[-1][:-17] + ".nii.gz")
sitk.WriteImage(sitk.ReadImage(i), out_fname)
# test
current_dir = join(folder, "test")
test_data = subfiles(current_dir, suffix="mhd")
for i in test_data:
out_fname = join(out_folder, "imagesTs", i.split("/")[-1][:-4] + "_0000.nii.gz")
sitk.WriteImage(sitk.ReadImage(i), out_fname)
json_dict = OrderedDict()
json_dict['name'] = "PROMISE12"
json_dict['description'] = "prostate"
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": "MRI",
}
json_dict['labels'] = {
"0": "background",
"1": "prostate"
}
json_dict['numTraining'] = len(raw_data)
json_dict['numTest'] = len(test_data)
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1][:-4], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1][:-4]} for i in
raw_data]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1][:-4] for i in test_data]
save_json(json_dict, os.path.join(out_folder, "dataset.json"))