def post_process(img: structure.BrainImage, segmentation: sitk.Image,
probability: sitk.Image, **kwargs) -> sitk.Image:
"""Post-processes a segmentation.
Args:
img (structure.BrainImage): The image.
segmentation (sitk.Image): The segmentation (label image).
probability (sitk.Image): The probabilities images (a vector image).
Returns:
sitk.Image: The post-processed image.
"""
print('-' * 10, 'Post-processing', img.id_)
# construct pipeline
pipeline = fltr.FilterPipeline()
if kwargs.get('crf_post', False):
pipeline.add_filter(fltr_postp.DenseCRF())
pipeline.set_param(
fltr_postp.DenseCRFParams(img.images[structure.BrainImageTypes.T1],
img.images[structure.BrainImageTypes.T2],
probability), 0)
return pipeline.execute(segmentation)
def post_process(img: structure.BrainImage, segmentation: sitk.Image, probability: sitk.Image,
**kwargs) -> sitk.Image:
"""Post-processes a segmentation.
Args:
img (structure.BrainImage): The image.
segmentation (sitk.Image): The segmentation (label image).
probability (sitk.Image): The probabilities images (a vector image).
Returns:
sitk.Image: The post-processed image.
"""
print('-' * 10, 'Post-processing', img.id_)
# construct pipeline
pipeline = fltr.FilterPipeline()
if kwargs.get('simple_post', False):
pipeline.add_filter(fltr_postp.ImagePostProcessing())
pipeline.set_param(fltr_postp.ImagePostProcessingParam(probability,
variance=kwargs.get('variance', 1.0),
preserve_background=kwargs.get('preserve_background', False)), len(pipeline.filters) - 1)
if kwargs.get('crf_post', False):
pipeline.add_filter(fltr_postp.DenseCRF())
pipeline.set_param(fltr_postp.DenseCRFParams(img.images[structure.BrainImageTypes.T1w],
img.images[structure.BrainImageTypes.T2w],
probability), len(pipeline.filters) - 1)
return pipeline.execute(segmentation)
