|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
from collections import OrderedDict |
|
from nnunet.paths import nnUNet_raw_data |
|
from batchgenerators.utilities.file_and_folder_operations import * |
|
import shutil |
|
from multiprocessing import Pool |
|
import nibabel |
|
|
|
|
|
def reorient(filename): |
|
img = nibabel.load(filename) |
|
img = nibabel.as_closest_canonical(img) |
|
nibabel.save(img, filename) |
|
|
|
|
|
if __name__ == "__main__": |
|
base = "/media/fabian/DeepLearningData/Pancreas-CT" |
|
|
|
|
|
p = Pool(8) |
|
results = [] |
|
|
|
for f in subfiles(join(base, "data"), suffix=".nii.gz"): |
|
results.append(p.map_async(reorient, (f, ))) |
|
_ = [i.get() for i in results] |
|
|
|
for f in subfiles(join(base, "TCIA_pancreas_labels-02-05-2017"), suffix=".nii.gz"): |
|
results.append(p.map_async(reorient, (f, ))) |
|
_ = [i.get() for i in results] |
|
|
|
task_id = 62 |
|
task_name = "NIHPancreas" |
|
|
|
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 = [] |
|
cases = list(range(1, 83)) |
|
folder_data = join(base, "data") |
|
folder_labels = join(base, "TCIA_pancreas_labels-02-05-2017") |
|
for c in cases: |
|
casename = "pancreas_%04.0d" % c |
|
shutil.copy(join(folder_data, "PANCREAS_%04.0d.nii.gz" % c), join(imagestr, casename + "_0000.nii.gz")) |
|
shutil.copy(join(folder_labels, "label%04.0d.nii.gz" % c), join(labelstr, casename + ".nii.gz")) |
|
train_patient_names.append(casename) |
|
|
|
json_dict = OrderedDict() |
|
json_dict['name'] = task_name |
|
json_dict['description'] = task_name |
|
json_dict['tensorImageSize'] = "4D" |
|
json_dict['reference'] = "see website" |
|
json_dict['licence'] = "see website" |
|
json_dict['release'] = "0.0" |
|
json_dict['modality'] = { |
|
"0": "CT", |
|
} |
|
json_dict['labels'] = { |
|
"0": "background", |
|
"1": "Pancreas", |
|
} |
|
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")) |
|
|
