ToReferenceSpace

`ToReferenceSpace`

Bases: SpatialTransform

Modify the spatial metadata so it matches a reference space.

This is useful, for example, to set meaningful spatial metadata of a neural network embedding, for visualization or further processing such as resampling a segmentation output.

Examples:

import torchio as tio image = tio.datasets.FPG().t1 embedding_tensor = my_network(image.tensor) # we lose metadata here embedding_image = tio.ToReferenceSpace.from_tensor(embedding_tensor, 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.

`from_tensor(tensor, reference)` `staticmethod`

Build a TorchIO image from a tensor and a reference image.

ToReferenceSpace