SequentialLabels

`SequentialLabels`

Bases: LabelTransform

Remap labels in a label map so they become consecutive.

For example, if a label map has labels (0, 3, 5), then this will apply a RemapLabels transform with remapping={3: 1, 5: 2}, and therefore the output image will have labels (0, 1, 2).

Examples:

>>> import torch
>>> import torchio as tio
>>> def get_image(*labels):
...     tensor = torch.as_tensor(labels).reshape(1, 1, 1, -1)
...     image = tio.LabelMap(tensor=tensor)
...     return image
...
>>> img_with_bg = get_image(0, 5, 10)
>>> transform = tio.SequentialLabels()
>>> transform(img_with_bg).data
tensor([[[[0, 1, 2]]]])
>>> img_without_bg = get_image(7, 11, 99)
>>> transform(img_without_bg).data
tensor([[[[0, 1, 2]]]])

Note

This transformation is always fully invertible .

Warning

The background is typically represented with the label 0. There will be zeros in the output image even if they are none in the input.

Parameters:

Name	Type	Description	Default
`masking_method`	`TypeMaskingMethod`	See `RemapLabels`.	`None`
`**kwargs`		See `Transform` for additional keyword arguments.	`{}`

`call(data)`

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name	Type	Description	Default
`data`	`TypeTransformInput`	Instance of `torchio.Subject`, 4D `torch.Tensor` or `numpy.ndarray` with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a `torchio.Image`, a NiBabel Nifti1 image or a `dict`. The output type is the same as the input type.	required

`to_hydra_config()`

Return a dictionary representation of the transform for Hydra instantiation.

SequentialLabels