Skip to content

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) 

__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.

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()