RandomAffineElasticDeformation

`RandomAffineElasticDeformation`

Bases: RandomTransform, SpatialTransform

Apply a RandomAffine and RandomElasticDeformation simultaneously.

Optimization to use only a single SimpleITK resampling. For additional details on the transformations, see RandomAffine and RandomElasticDeformation

Parameters:

Name	Type	Description	Default
`affine_first`	`bool`	Apply affine before elastic deformation.	`True`
`affine_kwargs`	`dict[str, Any] \| None`	See `RandomAffine` for kwargs.	`None`
`elastic_kwargs`	`dict[str, Any] \| None`	See `RandomElasticDeformation` for kwargs.	`None`
`**kwargs`		See `Transform` for additional keyword arguments.	`{}`

Examples:

>>> import torchio as tio
>>> image = tio.datasets.Colin27().t1
>>> affine_kwargs = {'scales': (0.9, 1.2), 'degrees': 15}
>>> elastic_kwargs = {'max_displacement': (17, 12, 2)}
>>> transform = tio.RandomAffineElasticDeformation(
...     affine_kwargs,
...     elastic_kwargs
... )
>>> transformed = transform(image)

`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.

plot

Source code

import torchio as tio
subject = tio.datasets.Slicer('CTChest')
ct = subject.CT_chest
elastic_kwargs = {'max_displacement': (17, 12, 2)}
transform = tio.RandomAffineElasticDeformation(elastic_kwargs=elastic_kwargs)
ct_transformed = transform(ct)
subject.add_image(ct_transformed, 'Transformed')
subject.plot()

RandomAffineElasticDeformation