# 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 import subprocess import SimpleITK as sitk import numpy as np from batchgenerators.utilities.file_and_folder_operations import * from nnunet.dataset_conversion.utils import generate_dataset_json from nnunet.paths import nnUNet_raw_data from nnunet.paths import preprocessing_output_dir from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name def increase_batch_size(plans_file: str, save_as: str, bs_factor: int): a = load_pickle(plans_file) stages = list(a['plans_per_stage'].keys()) for s in stages: a['plans_per_stage'][s]['batch_size'] *= bs_factor save_pickle(a, save_as) def prepare_submission(folder_in, folder_out): nii = subfiles(folder_in, suffix='.gz', join=False) maybe_mkdir_p(folder_out) for n in nii: i = n.split('-')[-1][:-10] shutil.copy(join(folder_in, n), join(folder_out, i + '.nii.gz')) def get_ids_from_folder(folder): cts = subfiles(folder, suffix='_ct.nii.gz', join=False) ids = [] for c in cts: ids.append(c.split('-')[-1][:-10]) return ids def postprocess_submission(folder_ct, folder_pred, folder_postprocessed, bbox_distance_to_seg_in_cm=7.5): """ segment with lung mask, get bbox from that, use bbox to remove predictions in background WE EXPERIMENTED WITH THAT ON THE VALIDATION SET AND FOUND THAT IT DOESN'T DO ANYTHING. NOT USED FOR TEST SET """ # pip install git+https://github.com/JoHof/lungmask cts = subfiles(folder_ct, suffix='_ct.nii.gz', join=False) output_files = [i[:-10] + '_lungmask.nii.gz' for i in cts] # run lungmask on everything for i, o in zip(cts, output_files): if not isfile(join(folder_ct, o)): subprocess.call(['lungmask', join(folder_ct, i), join(folder_ct, o), '--modelname', 'R231CovidWeb']) if not isdir(folder_postprocessed): maybe_mkdir_p(folder_postprocessed) ids = get_ids_from_folder(folder_ct) for i in ids: # find lungmask lungmask_file = join(folder_ct, 'volume-covid19-A-' + i + '_lungmask.nii.gz') if not isfile(lungmask_file): raise RuntimeError('missing lung') seg_file = join(folder_pred, 'volume-covid19-A-' + i + '_ct.nii.gz') if not isfile(seg_file): raise RuntimeError('missing seg') lung_mask = sitk.GetArrayFromImage(sitk.ReadImage(lungmask_file)) seg_itk = sitk.ReadImage(seg_file) seg = sitk.GetArrayFromImage(seg_itk) where = np.argwhere(lung_mask != 0) bbox = [ [min(where[:, 0]), max(where[:, 0])], [min(where[:, 1]), max(where[:, 1])], [min(where[:, 2]), max(where[:, 2])], ] spacing = np.array(seg_itk.GetSpacing())[::-1] # print(bbox) for dim in range(3): sp = spacing[dim] voxels_extend = max(int(np.ceil(bbox_distance_to_seg_in_cm / sp)), 1) bbox[dim][0] = max(0, bbox[dim][0] - voxels_extend) bbox[dim][1] = min(seg.shape[dim], bbox[dim][1] + voxels_extend) # print(bbox) seg_old = np.copy(seg) seg[0:bbox[0][0], :, :] = 0 seg[bbox[0][1]:, :, :] = 0 seg[:, 0:bbox[1][0], :] = 0 seg[:, bbox[1][1]:, :] = 0 seg[:, :, 0:bbox[2][0]] = 0 seg[:, :, bbox[2][1]:] = 0 if np.any(seg_old != seg): print('changed seg', i) argwhere = np.argwhere(seg != seg_old) print(argwhere[np.random.choice(len(argwhere), 10)]) seg_corr = sitk.GetImageFromArray(seg) seg_corr.CopyInformation(seg_itk) sitk.WriteImage(seg_corr, join(folder_postprocessed, 'volume-covid19-A-' + i + '_ct.nii.gz')) def manually_set_configurations(): """ ALSO NOT USED! :return: """ task115_dir = join(preprocessing_output_dir, convert_id_to_task_name(115)) ## larger patch size # task115 3d_fullres default is: """ {'batch_size': 2, 'num_pool_per_axis': [2, 6, 6], 'patch_size': array([ 28, 256, 256]), 'median_patient_size_in_voxels': array([ 62, 512, 512]), 'current_spacing': array([5. , 0.74199998, 0.74199998]), 'original_spacing': array([5. , 0.74199998, 0.74199998]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]} """ plans = load_pickle(join(task115_dir, 'nnUNetPlansv2.1_plans_3D.pkl')) fullres_stage = plans['plans_per_stage'][1] fullres_stage['patch_size'] = np.array([ 64, 320, 320]) fullres_stage['num_pool_per_axis'] = [4, 6, 6] fullres_stage['pool_op_kernel_sizes'] = [[1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]] fullres_stage['conv_kernel_sizes'] = [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]] save_pickle(plans, join(task115_dir, 'nnUNetPlansv2.1_custom_plans_3D.pkl')) ## larger batch size # (default for all 3d trainings is batch size 2) increase_batch_size(join(task115_dir, 'nnUNetPlansv2.1_plans_3D.pkl'), join(task115_dir, 'nnUNetPlansv2.1_bs3x_plans_3D.pkl'), 3) increase_batch_size(join(task115_dir, 'nnUNetPlansv2.1_plans_3D.pkl'), join(task115_dir, 'nnUNetPlansv2.1_bs5x_plans_3D.pkl'), 5) # residual unet """ default is: Out[7]: {'batch_size': 2, 'num_pool_per_axis': [2, 6, 5], 'patch_size': array([ 28, 256, 224]), 'median_patient_size_in_voxels': array([ 62, 512, 512]), 'current_spacing': array([5. , 0.74199998, 0.74199998]), 'original_spacing': array([5. , 0.74199998, 0.74199998]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 1]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'num_blocks_encoder': (1, 2, 3, 4, 4, 4, 4), 'num_blocks_decoder': (1, 1, 1, 1, 1, 1)} """ plans = load_pickle(join(task115_dir, 'nnUNetPlans_FabiansResUNet_v2.1_plans_3D.pkl')) fullres_stage = plans['plans_per_stage'][1] fullres_stage['patch_size'] = np.array([ 56, 256, 256]) fullres_stage['num_pool_per_axis'] = [3, 6, 6] fullres_stage['pool_op_kernel_sizes'] = [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]] fullres_stage['conv_kernel_sizes'] = [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]] save_pickle(plans, join(task115_dir, 'nnUNetPlans_FabiansResUNet_v2.1_custom_plans_3D.pkl')) def check_same(img1: str, img2: str): """ checking initial vs corrected dataset :param img1: :param img2: :return: """ img1 = sitk.GetArrayFromImage(sitk.ReadImage(img1)) img2 = sitk.GetArrayFromImage(sitk.ReadImage(img2)) if not np.all([i==j for i, j in zip(img1.shape, img2.shape)]): print('shape') return False else: same = np.all(img1==img2) if same: return True else: diffs = np.argwhere(img1!=img2) print('content in', diffs.shape[0], 'voxels') print('random disagreements:') print(diffs[np.random.choice(len(diffs), min(3, diffs.shape[0]), replace=False)]) return False def check_dataset_same(dataset_old='/home/fabian/Downloads/COVID-19-20/Train', dataset_new='/home/fabian/data/COVID-19-20_officialCorrected/COVID-19-20_v2/Train'): """ :param dataset_old: :param dataset_new: :return: """ cases = [i[:-10] for i in subfiles(dataset_new, suffix='_ct.nii.gz', join=False)] for c in cases: data_file = join(dataset_old, c + '_ct_corrDouble.nii.gz') corrected_double = False if not isfile(data_file): data_file = join(dataset_old, c+'_ct.nii.gz') else: corrected_double = True data_file_new = join(dataset_new, c+'_ct.nii.gz') same = check_same(data_file, data_file_new) if not same: print('data differs in case', c, '\n') seg_file = join(dataset_old, c + '_seg_corrDouble_corrected.nii.gz') if not isfile(seg_file): seg_file = join(dataset_old, c + '_seg_corrected_auto.nii.gz') if isfile(seg_file): assert ~corrected_double else: seg_file = join(dataset_old, c + '_seg_corrected.nii.gz') if isfile(seg_file): assert ~corrected_double else: seg_file = join(dataset_old, c + '_seg_corrDouble.nii.gz') if isfile(seg_file): assert ~corrected_double else: seg_file = join(dataset_old, c + '_seg.nii.gz') seg_file_new = join(dataset_new, c + '_seg.nii.gz') same = check_same(seg_file, seg_file_new) if not same: print('seg differs in case', c, '\n') if __name__ == '__main__': # this is the folder containing the data as downloaded from https://covid-segmentation.grand-challenge.org/COVID-19-20/ # (zip file was decompressed!) downloaded_data_dir = '/home/fabian/data/COVID-19-20_officialCorrected/COVID-19-20_v2/' task_name = "Task115_COVIDSegChallenge" target_base = join(nnUNet_raw_data, task_name) target_imagesTr = join(target_base, "imagesTr") target_imagesVal = join(target_base, "imagesVal") target_labelsTr = join(target_base, "labelsTr") maybe_mkdir_p(target_imagesTr) maybe_mkdir_p(target_imagesVal) maybe_mkdir_p(target_labelsTr) train_orig = join(downloaded_data_dir, "Train") # convert training set cases = [i[:-10] for i in subfiles(train_orig, suffix='_ct.nii.gz', join=False)] for c in cases: data_file = join(train_orig, c+'_ct.nii.gz') # before there was the official corrected dataset we did some corrections of our own. These corrections were # dropped when the official dataset was revised. seg_file = join(train_orig, c + '_seg_corrected.nii.gz') if not isfile(seg_file): seg_file = join(train_orig, c + '_seg.nii.gz') 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 # 3d_fullres nnUNetTrainerV2_DA3_BN__nnUNetPlans_v2.1 0.7498 # 3d_fullres nnUNetTrainerV2_DA3__nnUNetPlans_v2.1 0.7532 # Test set prediction # nnUNet_predict -i COVID-19-20_TestSet -o covid_testset_predictions/3d_fullres/nnUNetTrainerV2_ResencUNet_DA3__nnUNetPlans_FabiansResUNet_v2.1 -tr nnUNetTrainerV2_ResencUNet_DA3 -p nnUNetPlans_FabiansResUNet_v2.1 -m 3d_fullres -f 0 1 2 3 4 5 6 7 8 9 -t 115 -z # nnUNet_predict -i COVID-19-20_TestSet -o covid_testset_predictions/3d_fullres/nnUNetTrainerV2_ResencUNet__nnUNetPlans_FabiansResUNet_v2.1 -tr nnUNetTrainerV2_ResencUNet -p nnUNetPlans_FabiansResUNet_v2.1 -m 3d_fullres -f 0 1 2 3 4 5 6 7 8 9 -t 115 -z # nnUNet_predict -i COVID-19-20_TestSet -o covid_testset_predictions/3d_lowres/nnUNetTrainerV2_ResencUNet_DA3_BN__nnUNetPlans_FabiansResUNet_v2.1 -tr nnUNetTrainerV2_ResencUNet_DA3_BN -p nnUNetPlans_FabiansResUNet_v2.1 -m 3d_lowres -f 0 1 2 3 4 5 6 7 8 9 -t 115 -z # nnUNet_predict -i COVID-19-20_TestSet -o covid_testset_predictions/3d_fullres/nnUNetTrainerV2_DA3_BN__nnUNetPlans_v2.1 -tr nnUNetTrainerV2_DA3_BN -m 3d_fullres -f 0 1 2 3 4 5 6 7 8 9 -t 115 -z # nnUNet_predict -i COVID-19-20_TestSet -o covid_testset_predictions/3d_fullres/nnUNetTrainerV2_DA3__nnUNetPlans_v2.1 -tr nnUNetTrainerV2_DA3 -m 3d_fullres -f 0 1 2 3 4 5 6 7 8 9 -t 115 -z # nnUNet_ensemble -f 3d_lowres/nnUNetTrainerV2_ResencUNet_DA3_BN__nnUNetPlans_FabiansResUNet_v2.1/ 3d_fullres/nnUNetTrainerV2_ResencUNet__nnUNetPlans_FabiansResUNet_v2.1/ 3d_fullres/nnUNetTrainerV2_ResencUNet_DA3__nnUNetPlans_FabiansResUNet_v2.1/ 3d_fullres/nnUNetTrainerV2_DA3_BN__nnUNetPlans_v2.1/ 3d_fullres/nnUNetTrainerV2_DA3__nnUNetPlans_v2.1/ -o ensembled