for i in identifiers:
shutil.copy(join(imagests_source, i + "_C0.nii.gz"),
join(imagests, i + '_0000.nii.gz'))
shutil.copy(join(imagests_source, i + "_DE.nii.gz"),
join(imagests, i + '_0001.nii.gz'))
shutil.copy(join(imagests_source, i + "_T2.nii.gz"),
join(imagests, i + '_0002.nii.gz'))
generate_dataset_json(
join(out_base, 'dataset.json'),
imagestr,
None, ('C0', 'DE', 'T2'), {
0: 'background',
1: "left ventricular (LV) blood pool",
2: "right ventricular blood pool",
3: "LV normal myocardium",
4: "LV myocardial edema",
5: "LV myocardial scars",
},
task_name,
license=
'see http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/index.html',
dataset_description=
'see http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/index.html',
dataset_reference=
'http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/index.html',
dataset_release='0')
# REMEMBER THAT TEST SET INFERENCE WILL REQUIRE YOU CONVERT THE LABELS BACK TO THEIR CONVENTION
# use convert_labels_back_to_myops for that!
# man I am such a nice person. Love you guys.
images_dir_ts = join(base, 'testing', 'input')
testing_cases = subfiles(labels_dir_ts, suffix='.png', join=False)
for ts in testing_cases:
unique_name = ts[:-4]
input_segmentation_file = join(labels_dir_ts, ts)
input_image_file = join(images_dir_ts, ts)
output_image_file = join(target_imagesTs, unique_name)
output_seg_file = join(target_labelsTs, unique_name)
convert_2d_image_to_nifti(input_image_file, output_image_file, is_seg=False)
convert_2d_image_to_nifti(input_segmentation_file, output_seg_file, is_seg=True,
transform=lambda x: (x == 255).astype(int))
# finally we can call the utility for generating a dataset.json
generate_dataset_json(join(target_base, 'dataset.json'), target_imagesTr, target_imagesTs, ('Red', 'Green', 'Blue'),
labels={1: 'street'}, dataset_name=task_name, license='hands off!')
"""
once this is completed, you can use the dataset like any other nnU-Net dataset. Note that since this is a 2D
dataset there is no need to run preprocessing for 3D U-Nets. You should therefore run the
`nnUNet_plan_and_preprocess` command like this:
> nnUNet_plan_and_preprocess -t 120 -pl3d None
once that is completed, you can run the trainings as follows:
> nnUNet_train 2d nnUNetTrainerV2 120 FOLD
(where fold is again 0, 1, 2, 3 and 4 - 5-fold cross validation)
there is no need to run nnUNet_find_best_configuration because there is only one model to shoose from.
Note that without running nnUNet_find_best_configuration, nnU-Net will not have determined a postprocessing
imagesTr_path = os.path.join(dataset_folder, "imagesTr")
labelsTr_path = os.path.join(dataset_folder, "labelsTr")
imagesTs_path = os.path.join(dataset_folder, "imagesTs")
###
imagesTr_example = utils.get_identifiers_from_covid_GC(imagesTr_path)
print("+ imagesTr: ", imagesTr_example)
print("+ imagesTr: ", len(imagesTr_example))
# df_modality = pd.read_csv(os.path.join(radiomics_folder, "lesion_features-0.csv") , sep=',')
## Dataset conversion params
output_file = os.path.join(dataset_folder, "dataset.json")
imagesTr_dir = os.path.join(dataset_folder, "imagesTr")
imagesTs_dir = os.path.join(dataset_folder, "imagesTs")
modalities = ["CT"]
labels = {0: 'background', 1: 'lesion'}
dataset_name = "Task115_COVID-19",
license = "Hands on",
dataset_description = "Lesion segmentation for covid+",
dataset_reference = "COVID-19-20 - Grand Challenge & MICCAI",
dataset_release = '0.0'
utils.generate_dataset_json(output_file, imagesTr_dir, imagesTs_dir,
modalities, labels, dataset_name, license,
dataset_description, dataset_reference,
dataset_release)
## nnUNET Laucher
## Step-1: nnUNet_plan_and_preprocess -t 115 --verify_dataset_integrity
## Step-2: nnUNet_train 3d_fullres nnUNetTrainerV2 115 0
shutil.copy(data_file, join(target_imagesTr, c + "_0000.nii.gz"))
shutil.copy(seg_file, join(target_labelsTr, c + '.nii.gz'))
val_orig = join(downloaded_data_dir, "Validation")
cases = [i[:-10] for i in subfiles(val_orig, suffix='_ct.nii.gz', join=False)]
for c in cases:
data_file = join(val_orig, c + '_ct.nii.gz')
shutil.copy(data_file, join(target_imagesVal, c + "_0000.nii.gz"))
generate_dataset_json(
join(target_base, 'dataset.json'),
target_imagesTr,
None,
("CT", ),
{0: 'background', 1: 'covid'},
task_name,
dataset_reference='https://covid-segmentation.grand-challenge.org/COVID-19-20/'
)
# performance summary (train set 5-fold cross-validation)
# baselines
# 3d_fullres nnUNetTrainerV2__nnUNetPlans_v2.1 0.7441
# 3d_lowres nnUNetTrainerV2__nnUNetPlans_v2.1 0.745
# models used for test set prediction
# 3d_fullres nnUNetTrainerV2_ResencUNet_DA3__nnUNetPlans_FabiansResUNet_v2.1 0.7543
# 3d_fullres nnUNetTrainerV2_ResencUNet__nnUNetPlans_FabiansResUNet_v2.1 0.7527
# 3d_lowres nnUNetTrainerV2_ResencUNet_DA3_BN__nnUNetPlans_FabiansResUNet_v2.1 0.7513
labelstr = join(out_base, "labelsTr")
maybe_mkdir_p(imagestr)
maybe_mkdir_p(labelstr)
case_ids = subdirs(kits_data_dir, prefix='case_', join=False)
for c in case_ids:
if isfile(join(kits_data_dir, c, kits_segmentation_filename)):
shutil_sol.copyfile(
join(kits_data_dir, c, kits_segmentation_filename),
join(labelstr, c + '.nii.gz'))
shutil_sol.copyfile(join(kits_data_dir, c, 'imaging.nii.gz'),
join(imagestr, c + '_0000.nii.gz'))
generate_dataset_json(
join(out_base, 'dataset.json'),
imagestr,
None, ('CT', ), {
0: 'background',
1: "kidney",
2: "tumor",
3: "cyst",
},
task_name,
license=
'see https://kits21.kits-challenge.org/participate#download-block',
dataset_description='see https://kits21.kits-challenge.org/',
dataset_reference=
'https://www.sciencedirect.com/science/article/abs/pii/S1361841520301857, '
'https://kits21.kits-challenge.org/',
dataset_release='0')