def init_evaluator() -> eval_.Evaluator:
"""Initializes an evaluator.
Args:
directory (str): The directory for the results file.
result_file_name (str): The result file name (CSV file).
Returns:
eval.Evaluator: An evaluator.
"""
# os.makedirs(directory, exist_ok=True) # generate result directory, if it does not exists
# evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance()]
# evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(), metric.Accuracy(), metric.CohenKappaCoefficient(), metric.ProbabilisticDistance()]
evaluation_metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(95), metric.CohenKappaCoefficient(), metric.Accuracy(),
metric.JaccardCoefficient(), metric.MutualInformation(), metric.Precision(), metric.VolumeSimilarity(), metric.AreaUnderCurve(),
metric.FalseNegative(),metric.FalsePositive(), metric.TruePositive(), metric.TrueNegative(),metric.Sensitivity(),metric.Specificity()]
evaluation_metrics=[metric.DiceCoefficient(),metric.JaccardCoefficient(),metric.SurfaceDiceOverlap(),metric.Accuracy(),
metric.FMeasure(),metric.CohenKappaCoefficient(),metric.VolumeSimilarity(),metric.MutualInformation(),metric.AreaUnderCurve(),
metric.HausdorffDistance()]
evaluator = eval_.SegmentationEvaluator(evaluation_metrics,{})
# evaluator.add_writer(eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
evaluator.add_label(1, 'WhiteMatter')
evaluator.add_label(2, 'GreyMatter')
evaluator.add_label(3, 'Hippocampus')
evaluator.add_label(4, 'Amygdala')
evaluator.add_label(5, 'Thalamus')
# warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
# you should add more metrics than just the Hausdorff distance!
return evaluator
def init_evaluator(directory: str,
result_file_name: str = 'results.csv') -> eval_.Evaluator:
"""Initializes an evaluator.
Args:
directory (str): The directory for the results file.
result_file_name (str): The result file name (CSV file).
Returns:
eval.Evaluator: An evaluator.
"""
os.makedirs(
directory,
exist_ok=True) # generate result directory, if it does not exists
evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
evaluator.add_writer(
eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
evaluator.add_label(1, "WhiteMatter")
evaluator.add_label(2, "GreyMatter")
evaluator.add_label(3, "Hippocampus")
evaluator.add_label(4, "Amygdala")
evaluator.add_label(5, "Thalamus")
evaluator.add_metric(metric.DiceCoefficient())
evaluator.add_metric(metric.Specificity())
evaluator.add_metric(metric.Sensitivity())
evaluator.add_metric(metric.HausdorffDistance())
return evaluator
def init_evaluator(directory: str,
result_file_name: str = 'results.csv') -> eval_.Evaluator:
"""Initializes an evaluator.
Args:
directory (str): The directory for the results file.
result_file_name (str): The result file name (CSV file).
Returns:
eval.Evaluator: An evaluator.
"""
os.makedirs(
directory,
exist_ok=True) # generate result directory, if it does not exists
evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
evaluator.add_writer(
eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
evaluator.add_label(1, 'WhiteMatter')
evaluator.add_label(2, 'GreyMatter')
evaluator.add_label(3, 'Hippocampus')
evaluator.add_label(4, 'Amygdala')
evaluator.add_label(5, 'Thalamus')
evaluator.metrics = [
metric.DiceCoefficient(),
metric.HausdorffDistance(95)
] # Solutions
# todo: add hausdorff distance, 95th percentile (see metric.HausdorffDistance)
# evaluator.add_metric(metric.HausdorffDistance(95))
# warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
return evaluator
def main(data_dir: str, result_file: str, result_summary_file: str):
# initialize metrics
metrics = [
metric.DiceCoefficient(),
metric.HausdorffDistance(percentile=95, metric='HDRFDST95'),
metric.VolumeSimilarity()
]
# define the labels to evaluate
labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
# get subjects to evaluate
subject_dirs = [
subject for subject in glob.glob(os.path.join(data_dir, '*'))
if os.path.isdir(subject)
and os.path.basename(subject).startswith('Subject')
]
for subject_dir in subject_dirs:
subject_id = os.path.basename(subject_dir)
print(f'Evaluating {subject_id}...')
# load ground truth image and create artificial prediction by erosion
ground_truth = sitk.ReadImage(
os.path.join(subject_dir, f'{subject_id}_GT.mha'))
prediction = ground_truth
for label_val in labels.keys():
# erode each label we are going to evaluate
prediction = sitk.BinaryErode(prediction, 1, sitk.sitkBall, 0,
label_val)
# evaluate the "prediction" against the ground truth
evaluator.evaluate(prediction, ground_truth, subject_id)
# use two writers to report the results
writer.CSVWriter(result_file).write(evaluator.results)
print('\nSubject-wise results...')
writer.ConsoleWriter().write(evaluator.results)
# report also mean and standard deviation among all subjects
functions = {'MEAN': np.mean, 'STD': np.std}
writer.CSVStatisticsWriter(result_summary_file,
functions=functions).write(evaluator.results)
print('\nAggregated statistic results...')
writer.ConsoleStatisticsWriter(functions=functions).write(
evaluator.results)
# clear results such that the evaluator is ready for the next evaluation
evaluator.clear()
def init_evaluator(write_to_console: bool = True,
csv_file: str = None,
calculate_distance_metrics: bool = False):
evaluator = eval.Evaluator(EvaluatorAggregator())
if write_to_console:
evaluator.add_writer(eval.ConsoleEvaluatorWriter(5))
if csv_file is not None:
evaluator.add_writer(eval.CSVEvaluatorWriter(csv_file))
if calculate_distance_metrics:
evaluator.metrics = [
pymia_metric.DiceCoefficient(),
pymia_metric.HausdorffDistance(),
pymia_metric.HausdorffDistance(percentile=95, metric='HDRFDST95'),
pymia_metric.VolumeSimilarity()
]
else:
evaluator.metrics = [
pymia_metric.DiceCoefficient(),
pymia_metric.VolumeSimilarity()
]
evaluator.add_label(1, cfg.FOREGROUND_NAME)
return evaluator
def init_evaluator() -> eval_.Evaluator:
"""Initializes an evaluator.
Returns:
eval.Evaluator: An evaluator.
"""
# initialize metrics
metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(95.0)]
# define the labels to evaluate
labels = {
1: 'WhiteMatter',
2: 'GreyMatter',
3: 'Hippocampus',
4: 'Amygdala',
5: 'Thalamus'
}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
return evaluator
def init_evaluator() -> eval_.Evaluator:
"""Initializes an evaluator.
Returns:
eval.Evaluator: An evaluator.
"""
# initialize metrics
metrics = [metric.DiceCoefficient(), metric.HausdorffDistance(percentile=95)]
# todo: add hausdorff distance, 95th percentile (see metric.HausdorffDistance)
# warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
# define the labels to evaluate
labels = {1: 'WhiteMatter',
2: 'GreyMatter',
3: 'Hippocampus',
4: 'Amygdala',
5: 'Thalamus'
}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
return evaluator
def init_evaluator(directory: object, result_file_name: object = 'results.csv') -> object:
"""Initializes an evaluator.
Args:
directory (str): The directory for the results file.
result_file_name (str): The result file name (CSV file).
Returns:
eval.Evaluator: An evaluator.
"""
os.makedirs(directory, exist_ok=True) # generate result directory, if it does not exists
evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
evaluator.add_writer(eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
evaluator.add_label(1, 'WhiteMatter')
evaluator.add_label(2, 'GreyMatter')
evaluator.add_label(3, 'Hippocampus')
evaluator.add_label(4, 'Amygdala')
evaluator.add_label(5, 'Thalamus')
evaluator.metrics = [metric.DiceCoefficient(), metric.HausdorffDistance()]
# warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
# you should add more metrics than just the Hausdorff distance!
return evaluator
def load_atlas_custom_images(wdpath):
# params_list = list(data_batch.items())
# print(params_list[0] )
t1w_list = []
t2w_list = []
gt_label_list = []
brain_mask_list = []
transform_list = []
#Load the train labels_native with their transform
for dirpath, subdirs, files in os.walk(wdpath):
# print("dirpath", dirpath)
# print("subdirs", subdirs)
# print("files", files)
for x in files:
if x.endswith("T1native.nii.gz"):
t1w_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
elif x.endswith("T2native.nii.gz"):
t2w_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
elif x.endswith("labels_native.nii.gz"):
gt_label_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
elif x.endswith("Brainmasknative.nii.gz"):
brain_mask_list.append(sitk.ReadImage(os.path.join(dirpath,
x)))
elif x.endswith("affine.txt"):
transform_list.append(
sitk.ReadTransform(os.path.join(dirpath, x)))
# else:
# print("Problem in CustomAtlas in folder", dirpath)
#Resample and thershold to get the label
white_matter_list = []
grey_matter_list = []
hippocampus_list = []
amygdala_list = []
thalamus_list = []
for i in range(0, len(gt_label_list)):
resample_img = sitk.Resample(gt_label_list[i], atlas_t1,
transform_list[i],
sitk.sitkNearestNeighbor, 0,
gt_label_list[i].GetPixelIDValue())
white_matter_list.append(sitk.Threshold(resample_img, 1, 1, 0))
grey_matter_list.append(sitk.Threshold(resample_img, 2, 2, 0))
hippocampus_list.append(sitk.Threshold(resample_img, 3, 3, 0))
amygdala_list.append(sitk.Threshold(resample_img, 4, 4, 0))
thalamus_list.append(sitk.Threshold(resample_img, 5, 5, 0))
#Save each label from first data
path_to_save = '../bin/custom_atlas_result/'
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
sitk.WriteImage(hippocampus_list[0],
os.path.join(path_to_save, 'Hippocampus_label.nii'), True)
sitk.WriteImage(white_matter_list[0],
os.path.join(path_to_save, 'White_matter_label.nii'), True)
sitk.WriteImage(grey_matter_list[0],
os.path.join(path_to_save, 'Grey_matter_label.nii'), True)
sitk.WriteImage(amygdala_list[0],
os.path.join(path_to_save, 'Amygdala_label.nii'), True)
sitk.WriteImage(thalamus_list[0],
os.path.join(path_to_save, 'Thalamus_label.nii'), True)
#Save an image resampled to show segmentation
sitk.WriteImage(gt_label_list[0],
os.path.join(path_to_save, 'Train_image_1_resampled.nii'),
True)
# sum them up and divide by their number of images to make a probability map
white_matter_map = 0
grey_matter_map = 0
hippocampus_map = 0
amygdala_map = 0
thalamus_map = 0
for i in range(1, len(gt_label_list)):
white_matter_map = sitk.Add(white_matter_map, white_matter_list[i])
grey_matter_map = sitk.Add(grey_matter_map, grey_matter_list[i])
hippocampus_map = sitk.Add(hippocampus_map, hippocampus_list[i])
amygdala_map = sitk.Add(amygdala_map, amygdala_list[i])
thalamus_map = sitk.Add(thalamus_map, thalamus_list[i])
white_matter_map = sitk.Divide(white_matter_map, len(white_matter_list))
grey_matter_map = sitk.Divide(grey_matter_map, len(grey_matter_list))
hippocampus_map = sitk.Divide(hippocampus_map, len(hippocampus_list))
amygdala_map = sitk.Divide(amygdala_map, len(amygdala_list))
thalamus_map = sitk.Divide(thalamus_map, len(thalamus_list))
#atlas = sitk.Divide(sum_images, len(test_resample))
#slice = sitk.GetArrayFromImage(atlas)[90,:,:]
#plt.imshow(slice)
#Register without threshold
path_to_save = '../bin/custom_atlas_result/'
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
sitk.WriteImage(
grey_matter_map,
os.path.join(path_to_save, 'grey_matter_map_no_threshold.nii'), True)
sitk.WriteImage(
white_matter_map,
os.path.join(path_to_save, 'white_matter_map_no_threshold.nii'), True)
sitk.WriteImage(
hippocampus_map,
os.path.join(path_to_save, 'hippocampus_map_no_threshold.nii'), True)
sitk.WriteImage(
amygdala_map,
os.path.join(path_to_save, 'amygdala_map_no_threshold.nii'), True)
sitk.WriteImage(
thalamus_map,
os.path.join(path_to_save, 'thalamus_map_no_threshold.nii'), True)
#Threhold the 5 different maps to get a binary map
white_matter_map = sitk.BinaryThreshold(white_matter_map, 0.3, 1, 1, 0)
grey_matter_map = sitk.BinaryThreshold(grey_matter_map, 0.6, 2, 2, 0)
hippocampus_map = sitk.BinaryThreshold(hippocampus_map, 0.9, 3, 3, 0)
amygdala_map = sitk.BinaryThreshold(amygdala_map, 1.2, 4, 4, 0)
thalamus_map = sitk.BinaryThreshold(thalamus_map, 1.5, 5, 5, 0)
#Save the images
path_to_save = '../bin/custom_atlas_result/'
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
sitk.WriteImage(grey_matter_map,
os.path.join(path_to_save, 'grey_matter_map.nii'), True)
sitk.WriteImage(white_matter_map,
os.path.join(path_to_save, 'white_matter_map.nii'), True)
sitk.WriteImage(hippocampus_map,
os.path.join(path_to_save, 'hippocampus_map.nii'), True)
sitk.WriteImage(amygdala_map, os.path.join(path_to_save,
'amygdala_map.nii'), True)
sitk.WriteImage(thalamus_map, os.path.join(path_to_save,
'thalamus_map.nii'), True)
# Load the test labels_native and their transform
path_to_test = '../data/test'
test_gt_label_list = []
test_transform_list = []
for dirpath, subdirs, files in os.walk(path_to_test):
for x in files:
if x.endswith("labels_native.nii.gz"):
test_gt_label_list.append(
sitk.ReadImage(os.path.join(dirpath, x)))
if x.endswith("affine.txt"):
test_transform_list.append(
sitk.ReadTransform(os.path.join(dirpath, x)))
#Resample the labels_native with the transform
test_resample_img = []
for i in range(0, len(test_gt_label_list)):
resample_img = sitk.Resample(test_gt_label_list[i], atlas_t1,
test_transform_list[i],
sitk.sitkNearestNeighbor, 0,
test_gt_label_list[i].GetPixelIDValue())
test_resample_img.append(resample_img)
sitk.WriteImage(test_resample_img[0],
os.path.join(path_to_save, 'Test_data_1_resampled.nii'),
True)
# Save the first test patient labels
# path_to_save = '../bin/temp_test_result/'
# if not os.path.exists(path_to_save):
# os.makedirs(path_to_save)
# sitk.WriteImage(test_resample_img[0], os.path.join(path_to_save, 'FirstPatienFromTestList.nii'), False)
#Compute the dice coeefficent (and the Hausdorff distance)
label_list = [
'White Matter', 'Grey Matter', 'Hippocampus', 'Amygdala', 'Thalamus'
]
map_list = [
white_matter_map, grey_matter_map, hippocampus_map, amygdala_map,
thalamus_map
]
dice_list = []
path_to_save = '../bin/DiceTestResult/'
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
for i in range(0, 5):
evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
evaluator.metrics = [
metric.DiceCoefficient(),
metric.HausdorffDistance()
]
evaluator.add_writer(
eval_.CSVEvaluatorWriter(
os.path.join(path_to_save,
'DiceResults_' + label_list[i] + '.csv')))
evaluator.add_label(i + 1, label_list[i])
for j in range(0, len(test_resample_img)):
evaluator.evaluate(test_resample_img[j], map_list[i],
'Patient ' + str(j))
print("END Custom loadAtlas")
