def test_mask_example(self):
subject = self.sample_subject
negated_mask = subject.label.data.logical_not()
masked_voxel_indices = negated_mask.nonzero(as_tuple=True)
transform = tio.Mask(masking_method='label')
transformed = transform(subject)
assert (transformed.t1.data[masked_voxel_indices] == 0).all()
def test_mask_specified_label(self):
mask_label = [1]
negated_mask = self.sample_subject.label.data.logical_not()
masked_voxel_indices = negated_mask.nonzero(as_tuple=True)
transform = tio.Mask(masking_method='label', labels=mask_label)
transformed = transform(self.sample_subject)
assert (transformed.t1.data[masked_voxel_indices] == 0).all()
def test_mask_example(self):
subject = tio.datasets.Colin27()
negated_mask = subject.brain.data.logical_not()
masked_voxel_indices = negated_mask.nonzero(as_tuple=True)
transform = tio.Mask(masking_method='brain')
transformed = transform(subject)
assert (transformed.t1.data[masked_voxel_indices] == 0).all()
def test_single_mask_nonzero_background(self):
background_value = 314159
negated_mask = self.sample_subject.label.data.logical_not()
masked_voxel_indices = negated_mask.nonzero(as_tuple=True)
transform = tio.Mask(masking_method='label',
outside_value=background_value)
transformed = transform(self.sample_subject)
assert (transformed.t1.data[masked_voxel_indices] == background_value
).all()
def test_mask_specified_label_small(self):
def to_image(*numbers):
return torch.as_tensor(numbers).reshape(1, 1, 1, len(numbers))
image_tensor = to_image(1, 6, 7, 3, 0)
label_tensor = to_image(0, 1, 2, 3, 4)
mask_labels = [1, 2]
subject = tio.Subject(
image=tio.ScalarImage(tensor=image_tensor),
label=tio.LabelMap(tensor=label_tensor),
)
transform = tio.Mask(masking_method='label', labels=mask_labels)
transformed = transform(subject)
masked_list = transformed.image.data.flatten().tolist()
assert masked_list == [0, 6, 7, 0, 0]