def test_segmentation(self):
"""Check that segmentation works as expected from ported version."""
classes = ["fc", "tc", "pc", "men"]
expected_seg = np.load(
os.path.join(
util.UNITTEST_DATA_PATH,
"datasets/oai/expected/test_001_V00-iwoai-2019-t6.npy"))
scan = NiftiReader().load(
os.path.join(util.UNITTEST_DATA_PATH,
"datasets/oai/test_001_V00.nii.gz"))
tissue = FemoralCartilage()
tissue.find_weights(
os.path.join(os.path.dirname(__file__),
"../../weights/iwoai-2019-t6"))
dims = scan.volume.shape
input_shape = (dims[0], dims[1], 1)
model = IWOAIOAIUnet2D(input_shape=input_shape,
weights_path=tissue.weights_file_path)
masks = model.generate_mask(scan)
K.clear_session()
for i, tissue in enumerate(classes):
assert np.all(masks[tissue].volume == expected_seg[..., i])
def test_segmentation(self):
"""Test if batch size makes a difference on the segmentation output"""
scan = self.SCAN_TYPE(dicom_path=self.dicom_dirpath)
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER)
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
scan.segment(model, tissue)
def test_segmentation(self):
"""Test automatic segmentation
Expected: NotImplementedError
"""
scan = self.SCAN_TYPE(dicom_path=self.dicom_dirpath)
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER)
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
# automatic segmentation currently not implemented
with self.assertRaises(NotImplementedError):
scan.segment(model, tissue)
def test_segmentation_multiclass(self):
"""Test support for multiclass segmentation."""
scan = self.SCAN_TYPE.from_dicom(self.dicom_dirpath,
num_workers=util.num_workers())
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER),
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
scan.segment(model, tissue, use_rss=True)
# This should call __del__ in KerasSegModel
model = None
K.clear_session()
def test_segmentation(self):
classes = ["fc", "tc", "pc", "men"]
expected_seg = np.load(
os.path.join(
util.UNITTEST_DATA_PATH,
"datasets/oai/expected/test_001_V00-iwoai-2019-t6-normalized.npy",
))
scan = NiftiReader().load(
os.path.join(util.UNITTEST_DATA_PATH,
"datasets/oai/test_001_V00.nii.gz"))
tissue = FemoralCartilage()
tissue.find_weights(
os.path.join(os.path.dirname(__file__),
"../../weights/iwoai-2019-t6-normalized"))
dims = scan.volume.shape
input_shape = (dims[0], dims[1], 1)
model = IWOAIOAIUnet2DNormalized(input_shape=input_shape,
weights_path=tissue.weights_file_path)
masks = model.generate_mask(scan)
K.clear_session()
for i, tissue in enumerate(classes):
pred = masks[tissue].volume.astype(np.bool)
gt = expected_seg[..., i].astype(np.bool)
dice = 2 * np.sum(pred & gt) / np.sum(
pred.astype(np.uint8) + gt.astype(np.uint8))
# Zero-mean normalization of 32-bit vs 64-bit data results in slightly different
# estimations of the mean and standard deviation.
# However, when both volumes are compared pre-normalization
# using np.all() both volumes are the same (see :meth:`test_h5_nifti_same`).
# This slight difference in the image can affect network performance.
# As a placeholder, we assert that the dice between the expected and
# produced segmentations for each tissue must be greater than 99%
#
# Update: We found for this particular model and example pair, the segmentations
# achieve a dice score of 1.0 for all tissues.
# We enforce that the predicted mask must be equal to the expected mask.
assert dice >= 0.99, "{}: {:0.6f}".format(tissue, dice)
assert np.all(masks[tissue].volume == expected_seg[
..., i]), f"Segmentation not same for {tissue}"