Source code for torchio.transforms.preprocessing.label.keep_largest_component
import SimpleITK as sitk
from ....data.subject import Subject
from .label_transform import LabelTransform
[docs]
class KeepLargestComponent(LabelTransform):
r"""Keep only the largest connected component in a binary label map.
Args:
**kwargs: See :class:`~torchio.transforms.Transform` for additional
keyword arguments.
.. note:: For now, this transform only works for binary images, i.e., label
maps with a background and a foreground class. If you are interested in
extending this transform, please `open a new issue`_.
.. _open a new issue: https://github.com/fepegar/torchio/issues/new?assignees=&labels=enhancement&template=feature_request.md&title=Improve%20KeepLargestComponent%20transform
""" # noqa: B950
def apply_transform(self, subject: Subject) -> Subject:
for image in self.get_images(subject):
if image.num_channels > 1:
message = (
'The number of input channels must be 1,'
f' but it is {image.num_channels}'
)
raise RuntimeError(message)
sitk_image = image.as_sitk()
connected_components = sitk.ConnectedComponent(sitk_image)
labeled_cc = sitk.RelabelComponent(connected_components)
largest_cc = labeled_cc == 1
tensor, _ = self.sitk_to_nib(largest_cc)
image.set_data(tensor)
return subject