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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.
import shutil
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.paths import nnUNet_raw_data
if __name__ == "__main__":
"""
Nick asked me to rerun the training with other labels (the Kidney region is defined differently).
These labels operate in interpolated spacing. I don't like that but that's how it is
"""
base = "/media/fabian/My Book/datasets/KiTS_NicksLabels/kits19/data"
labelsdir = "/media/fabian/My Book/datasets/KiTS_NicksLabels/filled_labels"
task_id = 65
task_name = "KiTS_NicksLabels"
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_patient_names = []
test_patient_names = []
all_cases = subfolders(base, join=False)
train_patients = all_cases[:210]
test_patients = all_cases[210:]
for p in train_patients:
curr = join(base, p)
label_file = join(labelsdir, p + ".nii.gz")
image_file = join(curr, "imaging.nii.gz")
shutil.copy(image_file, join(imagestr, p + "_0000.nii.gz"))
shutil.copy(label_file, join(labelstr, p + ".nii.gz"))
train_patient_names.append(p)
for p in test_patients:
curr = join(base, p)
image_file = join(curr, "imaging.nii.gz")
shutil.copy(image_file, join(imagests, p + "_0000.nii.gz"))
test_patient_names.append(p)
json_dict = {}
json_dict['name'] = "KiTS"
json_dict['description'] = "kidney and kidney tumor segmentation"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "KiTS data for nnunet"
json_dict['licence'] = ""
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = {
"0": "background",
"1": "Kidney",
"2": "Tumor"
}
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1]} for i in
train_patient_names]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1] for i in test_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
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