def test_slice_exclusion_rules_none(
model_config: SegmentationModelBase) -> None:
"""
Test `apply_slice_exclusion_rules` if no rule is provided
"""
# create a copy as apply_slice_exclusion_rules modifies in place
segmentation_copy = np.copy(segmentation_single_overlap)
image_util.apply_slice_exclusion_rules(model_config, segmentation_copy)
assert np.array_equal(segmentation_copy, segmentation_single_overlap)
def test_slice_exclusion_rules_class_not_present(
model_config: SegmentationModelBase) -> None:
"""
Test `apply_slice_exclusion_rules` if the class to exclude is not present
"""
# create a copy as apply_slice_exclusion_rules modifies in place
segmentation_copy = np.copy(segmentation_class_not_present)
image_util.apply_slice_exclusion_rules(model_config, segmentation_copy)
assert np.array_equal(segmentation_copy, segmentation_class_not_present)
def test_slice_exclusion_rules_no_overlap(
model_config: SegmentationModelBase) -> None:
"""
Test `apply_slice_exclusion_rules` in the no overlap case
"""
# create a copy as apply_slice_exclusion_rules modifies in place
segmentation_copy = np.copy(segmentation_no_overlap)
image_util.apply_slice_exclusion_rules(model_config, segmentation_copy)
assert np.array_equal(segmentation_copy, segmentation_no_overlap)
def test_slice_exclusion_rules_multiple_overlap(
model_config: SegmentationModelBase,
expected_segmentation: np.ndarray) -> None:
"""
Test `apply_slice_exclusion_rules` in the multiple overlap case
"""
# create a copy as apply_slice_exclusion_rules modifies in place
segmentation_copy = np.copy(segmentation_multiple_overlap)
image_util.apply_slice_exclusion_rules(model_config, segmentation_copy)
assert np.array_equal(segmentation_copy, expected_segmentation)
def post_process(
self,
results: InferencePipeline.Result) -> InferencePipeline.Result:
"""
Perform connected component analysis to update segmentation with largest
connected component based on the configurations
:param results: inference results to post-process
:return: post-processed version of results
"""
if self.model_config.posterior_smoothing_mm:
posteriors = gaussian_smooth_posteriors(
posteriors=results.posteriors,
kernel_size_mm=self.model_config.posterior_smoothing_mm,
voxel_spacing_mm=results.voxel_spacing_mm)
results = InferencePipeline.Result(
epoch=results.epoch,
patient_id=results.patient_id,
posteriors=posteriors,
segmentation=posteriors_to_segmentation(posteriors),
voxel_spacing_mm=results.voxel_spacing_mm)
if self.model_config.summed_probability_rules and not self.model_config.disable_extra_postprocessing:
assert isinstance(self.model_config, SegmentationModelBase)
results = results.with_new_segmentation(
image_util.apply_summed_probability_rules(
self.model_config, results.posteriors,
results.segmentation))
if self.model_config.largest_connected_component_foreground_classes is not None:
# get indices for classes to restrict
restrict_class_indices_and_thresholds = []
for name, idx in self.model_config.class_and_index_with_background(
).items():
for name2, threshold in self.model_config.largest_connected_component_foreground_classes:
if name2 == name:
restrict_class_indices_and_thresholds.append(
(idx, threshold))
results = results.with_new_segmentation(
image_util.extract_largest_foreground_connected_component(
multi_label_array=results.segmentation,
# mypy gets confused below because List is invariant. Sequence is covariant
# but does not allow "append".
restrictions=restrict_class_indices_and_thresholds)
) # type: ignore
if self.model_config.slice_exclusion_rules and not self.model_config.disable_extra_postprocessing:
results = results.with_new_segmentation(
image_util.apply_slice_exclusion_rules(self.model_config,
results.segmentation))
return results
