# 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 collections import OrderedDict import SimpleITK as sitk from multiprocessing.pool import Pool from nnunet.configuration import default_num_threads from nnunet.paths import nnUNet_raw_data from batchgenerators.utilities.file_and_folder_operations import * import shutil from medpy import metric import numpy as np from nnunet.utilities.image_reorientation import reorient_all_images_in_folder_to_ras def check_if_all_in_good_orientation(imagesTr_folder: str, labelsTr_folder: str, output_folder: str) -> None: maybe_mkdir_p(output_folder) filenames = subfiles(labelsTr_folder, suffix='.nii.gz', join=False) import matplotlib.pyplot as plt for n in filenames: img = sitk.GetArrayFromImage(sitk.ReadImage(join(imagesTr_folder, n[:-7] + '_0000.nii.gz'))) lab = sitk.GetArrayFromImage(sitk.ReadImage(join(labelsTr_folder, n))) assert np.all([i == j for i, j in zip(img.shape, lab.shape)]) z_slice = img.shape[0] // 2 img_slice = img[z_slice] lab_slice = lab[z_slice] lab_slice[lab_slice != 0] = 1 img_slice = img_slice - img_slice.min() img_slice = img_slice / img_slice.max() stacked = np.vstack((img_slice, lab_slice)) print(stacked.shape) plt.imsave(join(output_folder, n[:-7] + '.png'), stacked, cmap='gray') def evaluate_verse_case(sitk_file_ref:str, sitk_file_test:str): """ Only vertebra that are present in the reference will be evaluated :param sitk_file_ref: :param sitk_file_test: :return: """ gt_npy = sitk.GetArrayFromImage(sitk.ReadImage(sitk_file_ref)) pred_npy = sitk.GetArrayFromImage(sitk.ReadImage(sitk_file_test)) dice_scores = [] for label in range(1, 26): mask_gt = gt_npy == label if np.sum(mask_gt) > 0: mask_pred = pred_npy == label dc = metric.dc(mask_pred, mask_gt) else: dc = np.nan dice_scores.append(dc) return dice_scores def evaluate_verse_folder(folder_pred, folder_gt, out_json="/home/fabian/verse.json"): p = Pool(default_num_threads) files_gt_bare = subfiles(folder_gt, join=False) assert all([isfile(join(folder_pred, i)) for i in files_gt_bare]), "some files are missing in the predicted folder" files_pred = [join(folder_pred, i) for i in files_gt_bare] files_gt = [join(folder_gt, i) for i in files_gt_bare] results = p.starmap_async(evaluate_verse_case, zip(files_gt, files_pred)) results = results.get() dct = {i: j for i, j in zip(files_gt_bare, results)} results_stacked = np.vstack(results) results_mean = np.nanmean(results_stacked, 0) overall_mean = np.nanmean(results_mean) save_json((dct, list(results_mean), overall_mean), out_json) p.close() p.join() def print_unique_labels_and_their_volumes(image: str, print_only_if_vol_smaller_than: float = None): img = sitk.ReadImage(image) voxel_volume = np.prod(img.GetSpacing()) img_npy = sitk.GetArrayFromImage(img) uniques = [i for i in np.unique(img_npy) if i != 0] volumes = {i: np.sum(img_npy == i) * voxel_volume for i in uniques} print('') print(image.split('/')[-1]) print('uniques:', uniques) for k in volumes.keys(): v = volumes[k] if print_only_if_vol_smaller_than is not None and v > print_only_if_vol_smaller_than: pass else: print('k:', k, '\tvol:', volumes[k]) def remove_label(label_file: str, remove_this: int, replace_with: int = 0): img = sitk.ReadImage(label_file) img_npy = sitk.GetArrayFromImage(img) img_npy[img_npy == remove_this] = replace_with img2 = sitk.GetImageFromArray(img_npy) img2.CopyInformation(img) sitk.WriteImage(img2, label_file) if __name__ == "__main__": ### First we create a nnunet dataset from verse. After this the images will be all willy nilly in their # orientation because that's how VerSe comes base = '/media/fabian/DeepLearningData/VerSe2019' base = "/home/fabian/data/VerSe2019" # correct orientation train_files_base = subfiles(join(base, "train"), join=False, suffix="_seg.nii.gz") train_segs = [i[:-len("_seg.nii.gz")] + "_seg.nii.gz" for i in train_files_base] train_data = [i[:-len("_seg.nii.gz")] + ".nii.gz" for i in train_files_base] test_files_base = [i[:-len(".nii.gz")] for i in subfiles(join(base, "test"), join=False, suffix=".nii.gz")] test_data = [i + ".nii.gz" for i in test_files_base] task_id = 56 task_name = "VerSe" 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 = [i[:-len("_seg.nii.gz")] for i in subfiles(join(base, "train"), join=False, suffix="_seg.nii.gz")] for p in train_patient_names: curr = join(base, "train") label_file = join(curr, p + "_seg.nii.gz") image_file = join(curr, p + ".nii.gz") shutil.copy(image_file, join(imagestr, p + "_0000.nii.gz")) shutil.copy(label_file, join(labelstr, p + ".nii.gz")) test_patient_names = [i[:-7] for i in subfiles(join(base, "test"), join=False, suffix=".nii.gz")] for p in test_patient_names: curr = join(base, "test") image_file = join(curr, p + ".nii.gz") shutil.copy(image_file, join(imagests, p + "_0000.nii.gz")) json_dict = OrderedDict() json_dict['name'] = "VerSe2019" json_dict['description'] = "VerSe2019" json_dict['tensorImageSize'] = "4D" json_dict['reference'] = "see challenge website" json_dict['licence'] = "see challenge website" json_dict['release'] = "0.0" json_dict['modality'] = { "0": "CT", } json_dict['labels'] = {i: str(i) for i in range(26)} 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")) # now we reorient all those images to ras. This saves a pkl with the original affine. We need this information to # bring our predictions into the same geometry for submission reorient_all_images_in_folder_to_ras(imagestr) reorient_all_images_in_folder_to_ras(imagests) reorient_all_images_in_folder_to_ras(labelstr) # sanity check check_if_all_in_good_orientation(imagestr, labelstr, join(out_base, 'sanitycheck')) # looks good to me - proceed # check the volumes of the vertebrae _ = [print_unique_labels_and_their_volumes(i, 1000) for i in subfiles(labelstr, suffix='.nii.gz')] # some cases appear fishy. For example, verse063.nii.gz has labels [1, 20, 21, 22, 23, 24] and 1 only has a volume # of 63mm^3 #let's correct those # 19 is connected to the image border and should not be segmented. Only one slice of 19 is segmented in the # reference. Looks wrong remove_label(join(labelstr, 'verse031.nii.gz'), 19, 0) # spurious annotation of 18 (vol: 8.00) remove_label(join(labelstr, 'verse060.nii.gz'), 18, 0) # spurious annotation of 16 (vol: 3.00) remove_label(join(labelstr, 'verse061.nii.gz'), 16, 0) # spurious annotation of 1 (vol: 63.00) although the rest of the vertebra is [20, 21, 22, 23, 24] remove_label(join(labelstr, 'verse063.nii.gz'), 1, 0) # spurious annotation of 3 (vol: 9.53) although the rest of the vertebra is # [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] remove_label(join(labelstr, 'verse074.nii.gz'), 3, 0) # spurious annotation of 3 (vol: 15.00) remove_label(join(labelstr, 'verse097.nii.gz'), 3, 0) # spurious annotation of 3 (vol: 10) although the rest of the vertebra is # [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] remove_label(join(labelstr, 'verse151.nii.gz'), 3, 0) # spurious annotation of 25 (vol: 4) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8, 9] remove_label(join(labelstr, 'verse201.nii.gz'), 25, 0) # spurious annotation of 23 (vol: 8) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8] remove_label(join(labelstr, 'verse207.nii.gz'), 23, 0) # spurious annotation of 23 (vol: 12) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8, 9] remove_label(join(labelstr, 'verse208.nii.gz'), 23, 0) # spurious annotation of 23 (vol: 2) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8, 9] remove_label(join(labelstr, 'verse212.nii.gz'), 23, 0) # spurious annotation of 20 (vol: 4) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8, 9] remove_label(join(labelstr, 'verse214.nii.gz'), 20, 0) # spurious annotation of 23 (vol: 15) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8] remove_label(join(labelstr, 'verse223.nii.gz'), 23, 0) # spurious annotation of 23 (vol: 1) and 25 (vol: 7) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8, 9] remove_label(join(labelstr, 'verse226.nii.gz'), 23, 0) remove_label(join(labelstr, 'verse226.nii.gz'), 25, 0) # spurious annotation of 25 (vol: 27) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8] remove_label(join(labelstr, 'verse227.nii.gz'), 25, 0) # spurious annotation of 20 (vol: 24) although the rest of the vertebra is # [1, 2, 3, 4, 5, 6, 7, 8] remove_label(join(labelstr, 'verse232.nii.gz'), 20, 0) # Now we are ready to run nnU-Net """# run this part of the code once training is done folder_gt = "/media/fabian/My Book/MedicalDecathlon/nnUNet_raw_splitted/Task056_VerSe/labelsTr" folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_fullres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw" out_json = "/home/fabian/Task056_VerSe_3d_fullres_summary.json" evaluate_verse_folder(folder_pred, folder_gt, out_json) folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_lowres/Task056_VerSe/nnUNetTrainerV2__nnUNetPlansv2.1/cv_niftis_raw" out_json = "/home/fabian/Task056_VerSe_3d_lowres_summary.json" evaluate_verse_folder(folder_pred, folder_gt, out_json) folder_pred = "/home/fabian/drives/datasets/results/nnUNet/3d_cascade_fullres/Task056_VerSe/nnUNetTrainerV2CascadeFullRes__nnUNetPlansv2.1/cv_niftis_raw" out_json = "/home/fabian/Task056_VerSe_3d_cascade_fullres_summary.json" evaluate_verse_folder(folder_pred, folder_gt, out_json)"""