| import sys |
| import SimpleITK as sitk |
| import json |
| import glob |
| import os |
| from tqdm import tqdm |
| import numpy as np |
| import torch |
|
|
|
|
| |
| def get_ct_normalisation_values(ct_plan_path): |
| """ |
| Get the mean and standard deviation for CT normalisation. |
| """ |
| |
| with open(ct_plan_path, "r") as f: |
| ct_plan = json.load(f) |
|
|
| ct_mean = ct_plan['foreground_intensity_properties_per_channel']["0"]['mean'] |
| ct_std = ct_plan['foreground_intensity_properties_per_channel']["0"]['std'] |
| print(f"CT mean: {ct_mean}, CT std: {ct_std}") |
| return ct_mean, ct_std |
|
|
| def revert_normalisation(pred_path, ct_mean, ct_std, save_path=None, mask_path=None, mask_outside_value=-1000): |
| """ |
| Revert the normalisation of a CT image. |
| """ |
| if save_path is None: |
| save_path = pred_path + '_revert_norm' |
| os.makedirs(save_path, exist_ok=True) |
| imgs = glob.glob(os.path.join(pred_path, "*.mha")) |
| if mask_path: |
| print(f"Applying mask from {mask_path} with outside value {mask_outside_value}") |
| else: |
| print("No mask provided, normalisation will be applied to all images.") |
| for img in tqdm(imgs): |
| img_sitk = sitk.ReadImage(img) |
| img_array = sitk.GetArrayFromImage(img_sitk) |
| img_array = img_array * ct_std + ct_mean |
| img_sitk_reverted = sitk.GetImageFromArray(img_array) |
| img_sitk_reverted.CopyInformation(img_sitk) |
|
|
| |
| if mask_path: |
| filename = os.path.basename(img) |
| filename = filename.replace('_0000', '') if '_0000' in filename else filename |
| mask_itk = sitk.ReadImage(os.path.join(mask_path, filename)) |
| img_sitk_reverted = sitk.Mask(img_sitk_reverted, mask_itk, outsideValue=mask_outside_value) |
| sitk.WriteImage(img_sitk_reverted, os.path.join(save_path, os.path.basename(img))) |
| |
| |
| if __name__ == "__main__": |
| ct_plan_path = "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset251_synthrad2025_task1_CT_AB_pre_v2r_stitched_masked_synseg/nnUNetResEncUNetLPlans.json" |
| ct_mean, ct_std = get_ct_normalisation_values(ct_plan_path) |
| mask_path = "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset250_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked_synseg/masks_real" |
| pred_path = "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset250_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked_synseg/nnUNetTrainerMRCT_loss_seg__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation" |
| revert_normalisation(pred_path, ct_mean, ct_std, save_path=pred_path + "_revert_norm", mask_path=mask_path) |
|
|
