def __init__(
self,
keys: KeysCollection,
applied_labels: Union[Sequence[int], int],
independent: bool = True,
connectivity: Optional[int] = None,
) -> 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 on.
If only one channel. The pixel whose value is not in this list will remain unchanged.
If the data is in one-hot format, this is the channel indexes to apply transform.
independent: consider several labels as a whole or independent, default is `True`.
Example use case would be segment label 1 is liver and label 2 is liver tumor, in that case
you want this "independent" to be specified as False.
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.
"""
super().__init__(keys)
self.converter = KeepLargestConnectedComponent(applied_labels,
independent,
connectivity)
def __init__(
self,
keys: KeysCollection,
applied_labels,
independent: bool = True,
connectivity: Optional[int] = None,
output_postfix: str = "largestcc",
):
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
applied_labels (int, list or tuple of int): Labels for applying the connected component on.
If only one channel. The pixel whose value is not in this list will remain unchanged.
If the data is in one-hot format, this is used to determine what channels to apply.
independent (bool): consider several labels as a whole or independent, default is `True`.
Example use case would be segment label 1 is liver and label 2 is liver tumor, in that case
you want this "independent" to be specified as False.
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.
output_postfix: the postfix string to construct keys to store converted data.
for example: if the keys of input data is `label`, output_postfix is `largestcc`,
the output data keys will be: `label_largestcc`.
if set to None, will replace the original data with the same key.
"""
super().__init__(keys)
if output_postfix is not None and not isinstance(output_postfix, str):
raise ValueError("output_postfix must be a string.")
self.output_postfix = output_postfix
self.converter = KeepLargestConnectedComponent(applied_labels, independent, connectivity)
def __init__(
self,
keys: KeysCollection,
applied_labels: Optional[Union[Sequence[int], int]] = None,
is_onehot: Optional[bool] = None,
independent: bool = True,
connectivity: Optional[int] = None,
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.
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
)
def __init__(
self, keys, applied_values, independent=True, background=0, connectivity=None, output_postfix="largestcc",
):
"""
Args:
keys (hashable items): keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
applied_values (list or tuple of int): number list for applying the connected component on.
The pixel whose value is not in this list will remain unchanged.
independent (bool): consider several labels as a whole or independent, default is `True`.
Example use case would be segment label 1 is liver and label 2 is liver tumor, in that case
you want this "independent" to be specified as False.
background (int): Background pixel value. The over-segmented pixels will be set as this value.
connectivity (int): 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.
output_postfix (str): the postfix string to construct keys to store converted data.
for example: if the keys of input data is `label`, output_postfix is `largestcc`,
the output data keys will be: `label_largestcc`.
if set to None, will replace the original data with the same key.
"""
super().__init__(keys)
if output_postfix is not None and not isinstance(output_postfix, str):
raise ValueError("output_postfix must be a string.")
self.output_postfix = output_postfix
self.converter = KeepLargestConnectedComponent(applied_values, independent, background, connectivity)
