Class KeepLargestConnectedComponentd

monai/transforms/post/dictionary.py:218–270

Dictionary-based wrapper of :py:class:`monai.transforms.KeepLargestConnectedComponent`.

class KeepLargestConnectedComponentd(MapTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.KeepLargestConnectedComponent`.
    """

    backend = KeepLargestConnectedComponent.backend

    def __init__(
        self,
        keys: KeysCollection,
        applied_labels: Sequence[int] | int | None = None,
        is_onehot: bool | None = None,
        independent: bool = True,
        connectivity: int | None = None,
        num_components: int = 1,
        allow_missing_keys: bool = False,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be transformed.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            applied_labels: Labels for applying the connected component analysis on.
                If given, voxels whose value is in this list will be analyzed.
                If `None`, all non-zero values will be analyzed.
            is_onehot: if `True`, treat the input data as OneHot format data, otherwise, not OneHot format data.
                default to None, which treats multi-channel data as OneHot and single channel data as not OneHot.
            independent: whether to treat ``applied_labels`` as a union of foreground labels.
                If ``True``, the connected component analysis will be performed on each foreground label independently
                and return the intersection of the largest components.
                If ``False``, the analysis will be performed on the union of foreground labels.
                default is `True`.
            connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
                Accepted values are ranging from  1 to input.ndim. If ``None``, a full
                connectivity of ``input.ndim`` is used. for more details:
                https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label.
            num_components: The number of largest components to preserve.
            allow_missing_keys: don't raise exception if key is missing.

        """
        super().__init__(keys, allow_missing_keys)
        self.converter = KeepLargestConnectedComponent(
            applied_labels=applied_labels,
            is_onehot=is_onehot,
            independent=independent,
            connectivity=connectivity,
            num_components=num_components,
        )

    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
        d = dict(data)
        for key in self.key_iterator(d):
            d[key] = self.converter(d[key])
        return d


class RemoveSmallObjectsd(MapTransform):