File size: 13,688 Bytes
19c1f58 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 |
from glob import glob
import os
from tqdm import tqdm
import SimpleITK as sitk
import pandas as pd
import json
from image_metrics import ImageMetricsCompute
import matplotlib.pyplot as plt
from pathlib import Path
import shutil
# def compute_folder_metrics(pred_path, gt_path, mask_path):
# """
# Analyze the results of the predictions.
# """
# pred_files = sorted(glob(os.path.join(pred_path, '*.mha')))
# # gt_path = os.path.join(raw_data_path, "gt_segmentations")
# # mask_path = os.path.join(raw_data_path, "masks")
# testing_metrics = ImageMetricsCompute()
# testing_metrics.init_storage(["mae", "psnr", "ms_ssim"])
# for pred_file in tqdm(pred_files):
# filename = os.path.basename(pred_file)
# gt_file = os.path.join(gt_path, filename)
# mask_file = os.path.join(mask_path, filename)
# img_pred = sitk.ReadImage(pred_file)
# img_gt = sitk.ReadImage(gt_file)
# img_mask = sitk.ReadImage(mask_file, sitk.sitkUInt8)
# array_pred = sitk.GetArrayFromImage(img_pred)
# array_gt = sitk.GetArrayFromImage(img_gt)
# array_mask = sitk.GetArrayFromImage(img_mask)
# res = testing_metrics.score_patient(array_gt, array_pred, array_mask)
# testing_metrics.add(res, filename)
# # aggregate results
# results = testing_metrics.aggregate()
# df = pd.DataFrame(
# {
# 'patient_id': testing_metrics.storage_id,
# 'mae': testing_metrics.storage['mae'],
# 'ms_ssim': testing_metrics.storage['ms_ssim'],
# 'psnr': testing_metrics.storage['psnr'],
# }
# )
# # save results, and df in the folder "results"
# results_path = os.path.join(pred_path, "results.json")
# df_path = os.path.join(pred_path, "results_individual.csv")
# os.makedirs(os.path.dirname(results_path), exist_ok=True)
# with open(results_path, 'w') as f:
# json.dump(results, f, indent=4)
# df.to_csv(df_path, index=False)
# return results, df
class ValidationResults():
"""
Class to analyze the results of the predictions.
It computes the metrics and saves the results in a folder.
This is used directly in the nnUNetTrainerMRCT class.
"""
def __init__(self, pred_path, gt_path, mask_path, save_path=None):
if not save_path:
save_path = pred_path+'_analysis'
print(f'Save path: {save_path}')
os.makedirs(save_path, exist_ok=True)
self.save_path = save_path
self.pred_path = pred_path
self.gt_path = gt_path
self.mask_path = mask_path
pred_files = sorted(glob(os.path.join(pred_path, '*.mha')))
self.patient_ids = [Path(pred_file).stem for pred_file in pred_files]
# init image metrics
self.test_metrics = ImageMetricsCompute()
self.test_metrics.init_storage(["mae", "psnr", "ms_ssim"])
def process_patients(self):
for patient_id in tqdm(self.patient_ids):
self.process_a_patient(patient_id)
dict_metric = self.analysis_patients()
return dict_metric
def analysis_patients(self):
# save aggregated metrics
dict_metric = self.test_metrics.aggregate()
with open(os.path.join(self.save_path, 'results_overall_masked.json'), 'w') as f:
json.dump(dict_metric, f, indent=4)
# save individual metric
df = pd.DataFrame(
{
'patient_id': self.test_metrics.storage_id,
'mae': self.test_metrics.storage['mae'],
'ms_ssim': self.test_metrics.storage['ms_ssim'],
'psnr': self.test_metrics.storage['psnr'],
}
)
df.to_csv(os.path.join(self.save_path, 'results_individual.csv'), index=True)
print("mean mae:", dict_metric['mae']['mean'])
print("mean psnr:", dict_metric['psnr']['mean'])
print("mean ms_ssim:", dict_metric['ms_ssim']['mean'])
return dict_metric
def process_a_patient(self, patient_id):
pred_path = os.path.join(self.pred_path, f'{patient_id}.mha')
gt_path = os.path.join(self.gt_path, f'{patient_id}.mha') #### to be changed; hand-coded here
mask_path = os.path.join(self.mask_path, f'{patient_id}.mha')
# read images
img_pred = sitk.ReadImage(pred_path, sitk.sitkFloat32)
img_gt = sitk.ReadImage(gt_path, sitk.sitkFloat32)
img_mask = sitk.ReadImage(mask_path, sitk.sitkUInt8)
# compute scores
array_pred = sitk.GetArrayFromImage(img_pred)
array_gt = sitk.GetArrayFromImage(img_gt)
array_mask = sitk.GetArrayFromImage(img_mask)
res = self.test_metrics.score_patient(array_gt, array_pred, array_mask)
self.test_metrics.add(res, patient_id)
class TestingResults():
def __init__(self, pred_path, raw_image_path, save_path=None):
if not save_path:
save_path = pred_path+'_analysis'
print(f'Save path: {save_path}')
os.makedirs(save_path, exist_ok=True)
self.pred_path = pred_path
self.save_path = save_path
self.raw_image_path = raw_image_path
pred_files = sorted(glob(os.path.join(pred_path, '*.mha')))
self.patient_ids = [Path(pred_file).stem for pred_file in pred_files]
self.col_names = ['src', 'pred', 'gt', 'mask', 'error']
# init image metrics
self.test_metrics = ImageMetricsCompute()
self.test_metrics.init_storage(["mae", "psnr", "ms_ssim"])
# init save sub-folders
self.slice_pc_to_save = [25, 50, 75]
for pc in self.slice_pc_to_save:
save_path_pc = os.path.join(self.save_path, '{}pc_png'.format(pc))
if not os.path.exists(save_path_pc):
os.makedirs(save_path_pc)
print('Make path: {}'.format(save_path_pc))
# all 3d images for analysis
self.save_path_all_3d_img = os.path.join(self.save_path, 'all_3d_img')
if not os.path.exists(self.save_path_all_3d_img):
os.makedirs(self.save_path_all_3d_img)
def process_patients(self):
for patient_id in tqdm(self.patient_ids):
self.process_a_patient(patient_id)
def analysis_patients(self):
# save aggregated metrics
dict_metric = self.test_metrics.aggregate()
with open(os.path.join(self.save_path, 'results_overall_masked.json'), 'w') as f:
json.dump(dict_metric, f, indent=4)
# save individual metric
df = pd.DataFrame(
{
'patient_id': self.test_metrics.storage_id,
'mae': self.test_metrics.storage['mae'],
'ms_ssim': self.test_metrics.storage['ms_ssim'],
'psnr': self.test_metrics.storage['psnr'],
}
)
df.to_csv(os.path.join(self.save_path, 'results_individual.csv'), index=True)
print("mean mae:", dict_metric['mae']['mean'])
print("mean psnr:", dict_metric['psnr']['mean'])
print("mean ms_ssim:", dict_metric['ms_ssim']['mean'])
def process_a_patient(self, patient_id):
pred_path = os.path.join(self.pred_path, f'{patient_id}.mha')
src_path = os.path.join(self.raw_image_path, patient_id, 'mr.mha')
gt_path = os.path.join(self.raw_image_path, patient_id, 'ct_deformed_padded.mha') #### to be changed; hand-coded here
mask_path = os.path.join(self.raw_image_path, patient_id, 'mask.mha')
# read images
img_src = sitk.ReadImage(src_path)
img_pred = sitk.ReadImage(pred_path, sitk.sitkFloat32)
img_gt = sitk.ReadImage(gt_path, sitk.sitkFloat32)
img_mask = sitk.ReadImage(mask_path, sitk.sitkUInt8)
# compute scores
array_src = sitk.GetArrayFromImage(img_src)
array_pred = sitk.GetArrayFromImage(img_pred)
array_gt = sitk.GetArrayFromImage(img_gt)
array_mask = sitk.GetArrayFromImage(img_mask)
res = self.test_metrics.score_patient(array_gt, array_pred, array_mask)
self.test_metrics.add(res, patient_id)
# save error images
self._save_error_image(img_pred, img_gt, img_mask, patient_id)
self._copy_images(pred_path, src_path, gt_path, mask_path, patient_id)
# save_png_slice
self._save_png_slice(array_src, array_pred, array_gt, array_mask, patient_id, pc=50)
self._save_png_slice(array_src, array_pred, array_gt, array_mask, patient_id, pc=25)
self._save_png_slice(array_src, array_pred, array_gt, array_mask, patient_id, pc=75)
plt.close('all')
def _save_error_image(self, img_pred, img_gt, img_mask, patient_id):
# save error images
img_err = sitk.AbsoluteValueDifference(img_pred, img_gt)
img_err = sitk.Mask(img_err, img_mask, outsideValue=0)
img_err.CopyInformation(img_pred)
sitk.WriteImage(img_err, os.path.join(self.save_path_all_3d_img, f'{patient_id}_error.mha'))
# print('Save Error images: ', os.path.join(save_err_path, f'{patient_id}.mha'))
def _copy_images(self, pred_path, src_path, gt_path, mask_path, patient_id):
shutil.copy(pred_path, os.path.join(self.save_path_all_3d_img, f'{patient_id}_pred.mha'))
shutil.copy(src_path, os.path.join(self.save_path_all_3d_img, f'{patient_id}_src.mha'))
shutil.copy(gt_path, os.path.join(self.save_path_all_3d_img, f'{patient_id}_gt.mha'))
shutil.copy(mask_path, os.path.join(self.save_path_all_3d_img, f'{patient_id}_mask.mha'))
def _save_png_slice(self, array_src, array_pred, array_gt, array_mask, patient_id, pc=50):
# init parameters
slice_a0 = int(array_gt.shape[0] * pc / 100)
slice_a1 = int(array_gt.shape[1] * pc / 100)
slice_a2 = int(array_gt.shape[2] * pc / 100)
rows = []
row_slices = [slice_a0, slice_a1, slice_a2]
# axial images
slice_a0_src = array_src[slice_a0, :, :]
slice_a0_pred = array_pred[slice_a0, :, :]
slice_a0_gt = array_gt[slice_a0, :, :]
slice_a0_mask = array_mask[slice_a0, :, :]
slice_a0_error = slice_a0_gt-slice_a0_pred
slice_a0_error[~slice_a0_mask.astype('bool')] = 0
row_0 = [slice_a0_src, slice_a0_pred, slice_a0_gt, slice_a0_mask, slice_a0_error]
rows.append(row_0)
# coronal images
slice_a1_src = array_src[:, slice_a1, :]
slice_a1_pred = array_pred[:, slice_a1, :]
slice_a1_gt = array_gt[:, slice_a1, :]
slice_a1_mask = array_mask[:, slice_a1, :]
slice_a1_error = slice_a1_gt - slice_a1_pred
slice_a1_error[~slice_a1_mask.astype('bool')] = 0
row_1 = [slice_a1_src, slice_a1_pred, slice_a1_gt, slice_a1_mask, slice_a1_error]
rows.append(row_1)
# sagital images
slice_a2_src = array_src[:, :, slice_a2]
slice_a2_pred = array_pred[:, :, slice_a2]
slice_a2_gt = array_gt[:, :, slice_a2]
slice_a2_mask = array_mask[:, :, slice_a2]
slice_a2_error = slice_a2_gt - slice_a2_pred
slice_a2_error[~slice_a2_mask.astype('bool')] = 0
row_2 = [slice_a2_src, slice_a2_pred, slice_a2_gt, slice_a2_mask, slice_a2_error]
rows.append(row_2)
# plot
fig, ax = plt.subplots(3, len(row_0), figsize=(15, 10))
for row in range(3):
for col in range(len(row_0)):
if col < 4:
if col ==1 or col == 2:
ax[row, col].imshow(rows[row][col], cmap='gray', vmin=-1024, vmax=2000)
else:
ax[row, col].imshow(rows[row][col], cmap='gray')
else:
ax[row, col].imshow(rows[row][col], cmap='twilight_shifted')
ax[row, col].axis('off')
ax[row, col].set_title('{}_slice{}'.format(self.col_names[col], row_slices[row]))
fig.subplots_adjust(wspace=0.05, top=0.8)
save_path = os.path.join(self.save_path, '{}pc_png' .format(pc))
if not os.path.exists(save_path):
os.makedirs(save_path)
fig.savefig(os.path.join(save_path, '{}.png'.format(patient_id)))
# print('Save png slices: ', save_path)
return fig
if __name__ == '__main__':
# pred_path = "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset206_synthrad2025_task1_MR_mednextL/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlans__3d_fullres/fold_0/validation"
# pred_path_revert_norm = pred_path + "_revert_norm"
# raw_data_path = f"/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset206_synthrad2025_task1_MR_mednextL"
# gt_path = os.path.join(raw_data_path, "gt_segmentations")
# mask_path = os.path.join(raw_data_path, "masks")
# results, df = compute_folder_metrics(pred_path_revert_norm, gt_path, mask_path)
# print("mean mae:", results['mae']['mean'])
# print("mean psnr:", results['psnr']['mean'])
# print("mean ms_ssim:", results['ms_ssim']['mean'])
input_path = "/datasets/work/hb-synthrad2023/source/synthrad2025_data_v2/synthRAD2025_Task1_Train/Task1/AB" # contain p_id/ct.mha, mask.mha, mr.mha
pred_path_revert_norm = "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset340_synthrad2025_task1_MR_AB_mednext/nnUNetTrainerV2_MedNeXt_L_kernel5__nnUNetPlans__3d_fullres/fold_0/validation_revert_norm"
vs = TestingResults(pred_path_revert_norm, task=1, region='AB')
vs.process_a_patient('1ABA011')
# vs.process_patients()
|