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.metrics = [metric.DiceCoefficient(),
metric.AreaUnderCurve(),
metric.VolumeSimilarity(),
metric.Accuracy(),
metric.AverageDistance(),
metric.CohenKappaMetric(),
metric.FalseNegative(),
metric.FalsePositive(),
metric.Fallout(),
metric.GroundTruthArea(),
metric.GroundTruthVolume(),
metric.Specificity(),
metric.Sensitivity()
]
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