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)`

Transform data and return a result of the same type.

Parameters:

Name	Type	Description	Default
`data`	`InputType`	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

`get_base_args()`

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

`add_base_args(arguments, overwrite_on_existing=False)`

Add the init args to existing arguments

`validate_keys_sequence(keys, name)` `staticmethod`

Ensure that the input is not a string but a sequence of strings.

`to_hydra_config()`

Return a dictionary representation of the transform for Hydra instantiation.

SequentialLabels