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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.
from copy import deepcopy
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
import SimpleITK as sitk
from multiprocessing import Pool
from medpy.metric import dc
import numpy as np
from nnunet.paths import network_training_output_dir
from scipy.ndimage import label
def compute_dice_scores(ref: str, pred: str):
ref = sitk.GetArrayFromImage(sitk.ReadImage(ref))
pred = sitk.GetArrayFromImage(sitk.ReadImage(pred))
kidney_mask_ref = ref > 0
kidney_mask_pred = pred > 0
if np.sum(kidney_mask_pred) == 0 and kidney_mask_ref.sum() == 0:
kidney_dice = np.nan
else:
kidney_dice = dc(kidney_mask_pred, kidney_mask_ref)
tumor_mask_ref = ref == 2
tumor_mask_pred = pred == 2
if np.sum(tumor_mask_ref) == 0 and tumor_mask_pred.sum() == 0:
tumor_dice = np.nan
else:
tumor_dice = dc(tumor_mask_ref, tumor_mask_pred)
geometric_mean = np.mean((kidney_dice, tumor_dice))
return kidney_dice, tumor_dice, geometric_mean
def evaluate_folder(folder_gt: str, folder_pred: str):
p = Pool(8)
niftis = subfiles(folder_gt, suffix=".nii.gz", join=False)
images_gt = [join(folder_gt, i) for i in niftis]
images_pred = [join(folder_pred, i) for i in niftis]
results = p.starmap(compute_dice_scores, zip(images_gt, images_pred))
p.close()
p.join()
with open(join(folder_pred, "results.csv"), 'w') as f:
for i, ni in enumerate(niftis):
f.write("%s,%0.4f,%0.4f,%0.4f\n" % (ni, *results[i]))
def remove_all_but_the_two_largest_conn_comp(img_itk_file: str, file_out: str):
"""
This was not used. I was just curious because others used this. Turns out this is not necessary for my networks
"""
img_itk = sitk.ReadImage(img_itk_file)
img_npy = sitk.GetArrayFromImage(img_itk)
labelmap, num_labels = label((img_npy > 0).astype(int))
if num_labels > 2:
label_sizes = []
for i in range(1, num_labels + 1):
label_sizes.append(np.sum(labelmap == i))
argsrt = np.argsort(label_sizes)[::-1] # two largest are now argsrt[0] and argsrt[1]
keep_mask = (labelmap == argsrt[0] + 1) | (labelmap == argsrt[1] + 1)
img_npy[~keep_mask] = 0
new = sitk.GetImageFromArray(img_npy)
new.CopyInformation(img_itk)
sitk.WriteImage(new, file_out)
print(os.path.basename(img_itk_file), num_labels, label_sizes)
else:
shutil.copy(img_itk_file, file_out)
def manual_postprocess(folder_in,
folder_out):
"""
This was not used. I was just curious because others used this. Turns out this is not necessary for my networks
"""
maybe_mkdir_p(folder_out)
infiles = subfiles(folder_in, suffix=".nii.gz", join=False)
outfiles = [join(folder_out, i) for i in infiles]
infiles = [join(folder_in, i) for i in infiles]
p = Pool(8)
_ = p.starmap_async(remove_all_but_the_two_largest_conn_comp, zip(infiles, outfiles))
_ = _.get()
p.close()
p.join()
def copy_npz_fom_valsets():
'''
this is preparation for ensembling
:return:
'''
base = join(network_training_output_dir, "3d_lowres/Task048_KiTS_clean")
folders = ['nnUNetTrainerNewCandidate23_FabiansPreActResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23_FabiansResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23__nnUNetPlans']
for f in folders:
out = join(base, f, 'crossval_npz')
maybe_mkdir_p(out)
shutil.copy(join(base, f, 'plans.pkl'), out)
for fold in range(5):
cur = join(base, f, 'fold_%d' % fold, 'validation_raw')
npz_files = subfiles(cur, suffix='.npz', join=False)
pkl_files = [i[:-3] + 'pkl' for i in npz_files]
assert all([isfile(join(cur, i)) for i in pkl_files])
for n in npz_files:
corresponding_pkl = n[:-3] + 'pkl'
shutil.copy(join(cur, n), out)
shutil.copy(join(cur, corresponding_pkl), out)
def ensemble(experiments=('nnUNetTrainerNewCandidate23_FabiansPreActResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23_FabiansResNet__nnUNetPlans'), out_dir="/media/fabian/Results/nnUNet/3d_lowres/Task048_KiTS_clean/ensemble_preactres_and_res"):
from nnunet.inference.ensemble_predictions import merge
folders = [join(network_training_output_dir, "3d_lowres/Task048_KiTS_clean", i, 'crossval_npz') for i in experiments]
merge(folders, out_dir, 8)
def prepare_submission(fld= "/home/fabian/drives/datasets/results/nnUNet/test_sets/Task048_KiTS_clean/predicted_ens_3d_fullres_3d_cascade_fullres_postprocessed", # '/home/fabian/datasets_fabian/predicted_KiTS_nnUNetTrainerNewCandidate23_FabiansResNet',
out='/home/fabian/drives/datasets/results/nnUNet/test_sets/Task048_KiTS_clean/submission'):
nii = subfiles(fld, join=False, suffix='.nii.gz')
maybe_mkdir_p(out)
for n in nii:
outfname = n.replace('case', 'prediction')
shutil.copy(join(fld, n), join(out, outfname))
def pretent_to_be_nnUNetTrainer(base, folds=(0, 1, 2, 3, 4)):
"""
changes best checkpoint pickle nnunettrainer class name to nnUNetTrainer
:param experiments:
:return:
"""
for fold in folds:
cur = join(base, "fold_%d" % fold)
pkl_file = join(cur, 'model_best.model.pkl')
a = load_pickle(pkl_file)
a['name_old'] = deepcopy(a['name'])
a['name'] = 'nnUNetTrainer'
save_pickle(a, pkl_file)
def reset_trainerName(base, folds=(0, 1, 2, 3, 4)):
for fold in folds:
cur = join(base, "fold_%d" % fold)
pkl_file = join(cur, 'model_best.model.pkl')
a = load_pickle(pkl_file)
a['name'] = a['name_old']
del a['name_old']
save_pickle(a, pkl_file)
def nnUNetTrainer_these(experiments=('nnUNetTrainerNewCandidate23_FabiansPreActResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23_FabiansResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23__nnUNetPlans')):
"""
changes best checkpoint pickle nnunettrainer class name to nnUNetTrainer
:param experiments:
:return:
"""
base = join(network_training_output_dir, "3d_lowres/Task048_KiTS_clean")
for exp in experiments:
cur = join(base, exp)
pretent_to_be_nnUNetTrainer(cur)
def reset_trainerName_these(experiments=('nnUNetTrainerNewCandidate23_FabiansPreActResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23_FabiansResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23__nnUNetPlans')):
"""
changes best checkpoint pickle nnunettrainer class name to nnUNetTrainer
:param experiments:
:return:
"""
base = join(network_training_output_dir, "3d_lowres/Task048_KiTS_clean")
for exp in experiments:
cur = join(base, exp)
reset_trainerName(cur)
if __name__ == "__main__":
base = "/media/fabian/My Book/datasets/KiTS2019_Challenge/kits19/data"
out = "/media/fabian/My Book/MedicalDecathlon/nnUNet_raw_splitted/Task040_KiTS"
cases = subdirs(base, join=False)
maybe_mkdir_p(out)
maybe_mkdir_p(join(out, "imagesTr"))
maybe_mkdir_p(join(out, "imagesTs"))
maybe_mkdir_p(join(out, "labelsTr"))
for c in cases:
case_id = int(c.split("_")[-1])
if case_id < 210:
shutil.copy(join(base, c, "imaging.nii.gz"), join(out, "imagesTr", c + "_0000.nii.gz"))
shutil.copy(join(base, c, "segmentation.nii.gz"), join(out, "labelsTr", c + ".nii.gz"))
else:
shutil.copy(join(base, c, "imaging.nii.gz"), join(out, "imagesTs", c + "_0000.nii.gz"))
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(cases)
json_dict['numTest'] = 0
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
cases]
json_dict['test'] = []
save_json(json_dict, os.path.join(out, "dataset.json"))
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