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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
from nnunet.paths import nnUNet_raw_data
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
if __name__ == "__main__":
base = "/media/yunlu/10TB/research/other_data/Multi-Atlas Labeling Beyond the Cranial Vault/RawData/"
task_id = 17
task_name = "AbdominalOrganSegmentation"
prefix = 'ABD'
foldername = "Task%03.0d_%s" % (task_id, task_name)
out_base = join(nnUNet_raw_data, 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)
train_folder = join(base, "Training/img")
label_folder = join(base, "Training/label")
test_folder = join(base, "Test/img")
train_patient_names = []
test_patient_names = []
train_patients = subfiles(train_folder, join=False, suffix = 'nii.gz')
for p in train_patients:
serial_number = int(p[3:7])
train_patient_name = f'{prefix}_{serial_number:03d}.nii.gz'
label_file = join(label_folder, f'label{p[3:]}')
image_file = join(train_folder, p)
shutil.copy(image_file, join(imagestr, f'{train_patient_name[:7]}_0000.nii.gz'))
shutil.copy(label_file, join(labelstr, train_patient_name))
train_patient_names.append(train_patient_name)
test_patients = subfiles(test_folder, join=False, suffix=".nii.gz")
for p in test_patients:
p = p[:-7]
image_file = join(test_folder, p + ".nii.gz")
serial_number = int(p[3:7])
test_patient_name = f'{prefix}_{serial_number:03d}.nii.gz'
shutil.copy(image_file, join(imagests, f'{test_patient_name[:7]}_0000.nii.gz'))
test_patient_names.append(test_patient_name)
json_dict = OrderedDict()
json_dict['name'] = "AbdominalOrganSegmentation"
json_dict['description'] = "Multi-Atlas Labeling Beyond the Cranial Vault Abdominal Organ Segmentation"
json_dict['tensorImageSize'] = "3D"
json_dict['reference'] = "https://www.synapse.org/#!Synapse:syn3193805/wiki/217789"
json_dict['licence'] = "see challenge website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = OrderedDict({
"00": "background",
"01": "spleen",
"02": "right kidney",
"03": "left kidney",
"04": "gallbladder",
"05": "esophagus",
"06": "liver",
"07": "stomach",
"08": "aorta",
"09": "inferior vena cava",
"10": "portal vein and splenic vein",
"11": "pancreas",
"12": "right adrenal gland",
"13": "left adrenal gland"}
)
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['training'] = [{'image': "./imagesTr/%s" % train_patient_name, "label": "./labelsTr/%s" % train_patient_name} for i, train_patient_name in enumerate(train_patient_names)]
json_dict['test'] = ["./imagesTs/%s" % test_patient_name for test_patient_name in test_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
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