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from multiprocessing.pool import Pool |
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import numpy as np |
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from collections import OrderedDict |
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from batchgenerators.utilities.file_and_folder_operations import * |
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from nnunet.dataset_conversion.Task043_BraTS_2019 import copy_BraTS_segmentation_and_convert_labels |
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from nnunet.paths import nnUNet_raw_data |
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import SimpleITK as sitk |
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import shutil |
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def convert_labels_back_to_BraTS(seg: np.ndarray): |
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new_seg = np.zeros_like(seg) |
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new_seg[seg == 1] = 2 |
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new_seg[seg == 3] = 4 |
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new_seg[seg == 2] = 1 |
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return new_seg |
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def load_convert_save(filename, input_folder, output_folder): |
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a = sitk.ReadImage(join(input_folder, filename)) |
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b = sitk.GetArrayFromImage(a) |
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c = convert_labels_back_to_BraTS(b) |
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d = sitk.GetImageFromArray(c) |
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d.CopyInformation(a) |
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sitk.WriteImage(d, join(output_folder, filename)) |
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def convert_labels_back_to_BraTS_2018_2019_convention(input_folder: str, output_folder: str, num_processes: int = 12): |
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""" |
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reads all prediction files (nifti) in the input folder, converts the labels back to BraTS convention and saves the |
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result in output_folder |
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:param input_folder: |
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:param output_folder: |
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:return: |
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""" |
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maybe_mkdir_p(output_folder) |
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nii = subfiles(input_folder, suffix='.nii.gz', join=False) |
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p = Pool(num_processes) |
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p.starmap(load_convert_save, zip(nii, [input_folder] * len(nii), [output_folder] * len(nii))) |
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p.close() |
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p.join() |
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if __name__ == "__main__": |
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""" |
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REMEMBER TO CONVERT LABELS BACK TO BRATS CONVENTION AFTER PREDICTION! |
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""" |
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task_name = "Task032_BraTS2018" |
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downloaded_data_dir = "/home/fabian/Downloads/BraTS2018_train_val_test_data/MICCAI_BraTS_2018_Data_Training" |
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target_base = join(nnUNet_raw_data, task_name) |
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target_imagesTr = join(target_base, "imagesTr") |
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target_imagesVal = join(target_base, "imagesVal") |
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target_imagesTs = join(target_base, "imagesTs") |
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target_labelsTr = join(target_base, "labelsTr") |
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maybe_mkdir_p(target_imagesTr) |
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maybe_mkdir_p(target_imagesVal) |
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maybe_mkdir_p(target_imagesTs) |
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maybe_mkdir_p(target_labelsTr) |
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patient_names = [] |
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for tpe in ["HGG", "LGG"]: |
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cur = join(downloaded_data_dir, tpe) |
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for p in subdirs(cur, join=False): |
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patdir = join(cur, p) |
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patient_name = tpe + "__" + p |
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patient_names.append(patient_name) |
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t1 = join(patdir, p + "_t1.nii.gz") |
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t1c = join(patdir, p + "_t1ce.nii.gz") |
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t2 = join(patdir, p + "_t2.nii.gz") |
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flair = join(patdir, p + "_flair.nii.gz") |
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seg = join(patdir, p + "_seg.nii.gz") |
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assert all([ |
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isfile(t1), |
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isfile(t1c), |
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isfile(t2), |
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isfile(flair), |
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isfile(seg) |
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]), "%s" % patient_name |
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shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz")) |
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shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz")) |
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shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz")) |
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shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz")) |
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copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz")) |
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json_dict = OrderedDict() |
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json_dict['name'] = "BraTS2018" |
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json_dict['description'] = "nothing" |
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json_dict['tensorImageSize'] = "4D" |
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json_dict['reference'] = "see BraTS2018" |
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json_dict['licence'] = "see BraTS2019 license" |
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json_dict['release'] = "0.0" |
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json_dict['modality'] = { |
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"0": "T1", |
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"1": "T1ce", |
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"2": "T2", |
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"3": "FLAIR" |
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} |
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json_dict['labels'] = { |
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"0": "background", |
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"1": "edema", |
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"2": "non-enhancing", |
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"3": "enhancing", |
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} |
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json_dict['numTraining'] = len(patient_names) |
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json_dict['numTest'] = 0 |
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json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in |
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patient_names] |
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json_dict['test'] = [] |
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save_json(json_dict, join(target_base, "dataset.json")) |
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del tpe, cur |
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downloaded_data_dir = "/home/fabian/Downloads/BraTS2018_train_val_test_data/MICCAI_BraTS_2018_Data_Validation" |
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for p in subdirs(downloaded_data_dir, join=False): |
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patdir = join(downloaded_data_dir, p) |
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patient_name = p |
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t1 = join(patdir, p + "_t1.nii.gz") |
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t1c = join(patdir, p + "_t1ce.nii.gz") |
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t2 = join(patdir, p + "_t2.nii.gz") |
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flair = join(patdir, p + "_flair.nii.gz") |
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assert all([ |
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isfile(t1), |
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isfile(t1c), |
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isfile(t2), |
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isfile(flair), |
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]), "%s" % patient_name |
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shutil.copy(t1, join(target_imagesVal, patient_name + "_0000.nii.gz")) |
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shutil.copy(t1c, join(target_imagesVal, patient_name + "_0001.nii.gz")) |
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shutil.copy(t2, join(target_imagesVal, patient_name + "_0002.nii.gz")) |
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shutil.copy(flair, join(target_imagesVal, patient_name + "_0003.nii.gz")) |
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downloaded_data_dir = "/home/fabian/Downloads/BraTS2018_train_val_test_data/MICCAI_BraTS_2018_Data_Testing_FIsensee" |
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for p in subdirs(downloaded_data_dir, join=False): |
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patdir = join(downloaded_data_dir, p) |
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patient_name = p |
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t1 = join(patdir, p + "_t1.nii.gz") |
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t1c = join(patdir, p + "_t1ce.nii.gz") |
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t2 = join(patdir, p + "_t2.nii.gz") |
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flair = join(patdir, p + "_flair.nii.gz") |
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assert all([ |
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isfile(t1), |
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isfile(t1c), |
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isfile(t2), |
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isfile(flair), |
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]), "%s" % patient_name |
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shutil.copy(t1, join(target_imagesTs, patient_name + "_0000.nii.gz")) |
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shutil.copy(t1c, join(target_imagesTs, patient_name + "_0001.nii.gz")) |
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shutil.copy(t2, join(target_imagesTs, patient_name + "_0002.nii.gz")) |
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shutil.copy(flair, join(target_imagesTs, patient_name + "_0003.nii.gz")) |
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