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nnUNet_calvingfront_detection / nnunet /dataset_conversion /Task043_BraTS_2019.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 numpy as np
from collections import OrderedDict
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.paths import nnUNet_raw_data
import SimpleITK as sitk
import shutil
def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
# use this for segmentation only!!!
# nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3
img = sitk.ReadImage(in_file)
img_npy = sitk.GetArrayFromImage(img)
uniques = np.unique(img_npy)
for u in uniques:
if u not in [0, 1, 2, 4]:
raise RuntimeError('unexpected label')
seg_new = np.zeros_like(img_npy)
seg_new[img_npy == 4] = 3
seg_new[img_npy == 2] = 1
seg_new[img_npy == 1] = 2
img_corr = sitk.GetImageFromArray(seg_new)
img_corr.CopyInformation(img)
sitk.WriteImage(img_corr, out_file)
if __name__ == "__main__":
"""
REMEMBER TO CONVERT LABELS BACK TO BRATS CONVENTION AFTER PREDICTION!
"""
task_name = "Task043_BraTS2019"
downloaded_data_dir = "/home/sdp/MLPERF/Brats2019_DATA/MICCAI_BraTS_2019_Data_Training"
target_base = join(nnUNet_raw_data, task_name)
target_imagesTr = join(target_base, "imagesTr")
target_imagesVal = join(target_base, "imagesVal")
target_imagesTs = join(target_base, "imagesTs")
target_labelsTr = join(target_base, "labelsTr")
maybe_mkdir_p(target_imagesTr)
maybe_mkdir_p(target_imagesVal)
maybe_mkdir_p(target_imagesTs)
maybe_mkdir_p(target_labelsTr)
patient_names = []
for tpe in ["HGG", "LGG"]:
cur = join(downloaded_data_dir, tpe)
for p in subdirs(cur, join=False):
patdir = join(cur, p)
patient_name = tpe + "__" + p
patient_names.append(patient_name)
t1 = join(patdir, p + "_t1.nii.gz")
t1c = join(patdir, p + "_t1ce.nii.gz")
t2 = join(patdir, p + "_t2.nii.gz")
flair = join(patdir, p + "_flair.nii.gz")
seg = join(patdir, p + "_seg.nii.gz")
assert all([
isfile(t1),
isfile(t1c),
isfile(t2),
isfile(flair),
isfile(seg)
]), "%s" % patient_name
shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz"))
shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz"))
shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz"))
shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz"))
copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz"))
json_dict = OrderedDict()
json_dict['name'] = "BraTS2019"
json_dict['description'] = "nothing"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "see BraTS2019"
json_dict['licence'] = "see BraTS2019 license"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "T1",
"1": "T1ce",
"2": "T2",
"3": "FLAIR"
}
json_dict['labels'] = {
"0": "background",
"1": "edema",
"2": "non-enhancing",
"3": "enhancing",
}
json_dict['numTraining'] = len(patient_names)
json_dict['numTest'] = 0
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
patient_names]
json_dict['test'] = []
save_json(json_dict, join(target_base, "dataset.json"))
downloaded_data_dir = "/home/sdp/MLPERF/Brats2019_DATA/MICCAI_BraTS_2019_Data_Validation"
for p in subdirs(downloaded_data_dir, join=False):
patdir = join(downloaded_data_dir, p)
patient_name = p
t1 = join(patdir, p + "_t1.nii.gz")
t1c = join(patdir, p + "_t1ce.nii.gz")
t2 = join(patdir, p + "_t2.nii.gz")
flair = join(patdir, p + "_flair.nii.gz")
assert all([
isfile(t1),
isfile(t1c),
isfile(t2),
isfile(flair),
]), "%s" % patient_name
shutil.copy(t1, join(target_imagesVal, patient_name + "_0000.nii.gz"))
shutil.copy(t1c, join(target_imagesVal, patient_name + "_0001.nii.gz"))
shutil.copy(t2, join(target_imagesVal, patient_name + "_0002.nii.gz"))
shutil.copy(flair, join(target_imagesVal, patient_name + "_0003.nii.gz"))
"""
#I dont have the testing data
downloaded_data_dir = "/home/fabian/Downloads/BraTS2018_train_val_test_data/MICCAI_BraTS_2018_Data_Testing_FIsensee"
for p in subdirs(downloaded_data_dir, join=False):
patdir = join(downloaded_data_dir, p)
patient_name = p
t1 = join(patdir, p + "_t1.nii.gz")
t1c = join(patdir, p + "_t1ce.nii.gz")
t2 = join(patdir, p + "_t2.nii.gz")
flair = join(patdir, p + "_flair.nii.gz")
assert all([
isfile(t1),
isfile(t1c),
isfile(t2),
isfile(flair),
]), "%s" % patient_name
shutil.copy(t1, join(target_imagesTs, patient_name + "_0000.nii.gz"))
shutil.copy(t1c, join(target_imagesTs, patient_name + "_0001.nii.gz"))
shutil.copy(t2, join(target_imagesTs, patient_name + "_0002.nii.gz"))
shutil.copy(flair, join(target_imagesTs, patient_name + "_0003.nii.gz"))"""