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nnUNet_calvingfront_detection / nnunet /dataset_conversion /Task083_VerSe2020.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.
import shutil
from collections import OrderedDict
from copy import deepcopy
from multiprocessing.pool import Pool
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.dataset_conversion.Task056_VerSe2019 import check_if_all_in_good_orientation, \
print_unique_labels_and_their_volumes
from nnunet.paths import nnUNet_raw_data, preprocessing_output_dir
from nnunet.utilities.image_reorientation import reorient_all_images_in_folder_to_ras
def manually_change_plans():
pp_out_folder = join(preprocessing_output_dir, "Task083_VerSe2020")
original_plans = join(pp_out_folder, "nnUNetPlansv2.1_plans_3D.pkl")
assert isfile(original_plans)
original_plans = load_pickle(original_plans)
# let's change the network topology for lowres and fullres
new_plans = deepcopy(original_plans)
stages = len(new_plans['plans_per_stage'])
for s in range(stages):
new_plans['plans_per_stage'][s]['patch_size'] = (224, 160, 160)
new_plans['plans_per_stage'][s]['pool_op_kernel_sizes'] = [[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]] # bottleneck of 7x5x5
new_plans['plans_per_stage'][s]['conv_kernel_sizes'] = [[3, 3, 3],
[3, 3, 3],
[3, 3, 3],
[3, 3, 3],
[3, 3, 3],
[3, 3, 3]]
save_pickle(new_plans, join(pp_out_folder, "custom_plans_3D.pkl"))
if __name__ == "__main__":
### First we create a nnunet dataset from verse. After this the images will be all willy nilly in their
# orientation because that's how VerSe comes
base = '/home/fabian/Downloads/osfstorage-archive/'
task_id = 83
task_name = "VerSe2020"
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 = []
for t in subdirs(join(base, 'training_data'), join=False):
train_patient_names_here = [i[:-len("_seg.nii.gz")] for i in
subfiles(join(base, "training_data", t), join=False, suffix="_seg.nii.gz")]
for p in train_patient_names_here:
curr = join(base, "training_data", t)
label_file = join(curr, p + "_seg.nii.gz")
image_file = join(curr, p + ".nii.gz")
shutil.copy(image_file, join(imagestr, p + "_0000.nii.gz"))
shutil.copy(label_file, join(labelstr, p + ".nii.gz"))
train_patient_names += train_patient_names_here
json_dict = OrderedDict()
json_dict['name'] = "VerSe2020"
json_dict['description'] = "VerSe2020"
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": "CT",
}
json_dict['labels'] = {i: str(i) for i in range(29)}
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = []
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 []]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
# now we reorient all those images to ras. This saves a pkl with the original affine. We need this information to
# bring our predictions into the same geometry for submission
reorient_all_images_in_folder_to_ras(imagestr, 16)
reorient_all_images_in_folder_to_ras(imagests, 16)
reorient_all_images_in_folder_to_ras(labelstr, 16)
# sanity check
check_if_all_in_good_orientation(imagestr, labelstr, join(out_base, 'sanitycheck'))
# looks good to me - proceed
# check the volumes of the vertebrae
p = Pool(6)
_ = p.starmap(print_unique_labels_and_their_volumes, zip(subfiles(labelstr, suffix='.nii.gz'), [1000] * 113))
# looks good
# Now we are ready to run nnU-Net
"""# run this part of the code once training is done
folder_gt = "/media/fabian/My Book/MedicalDecathlon/nnUNet_raw_splitted/Task056_VerSe/labelsTr"
folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_fullres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/fabian/Task056_VerSe_3d_fullres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)
folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_lowres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/fabian/Task056_VerSe_3d_lowres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)
folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_cascade_fullres/Task056_VerSe/nnUNetTrainerV2CascadeFullRes__nnUNetPlansv2.1/cv_niftis_raw"
out_json = "/home/fabian/Task056_VerSe_3d_cascade_fullres_summary.json"
evaluate_verse_folder(folder_pred, folder_gt, out_json)"""