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Augmentation

Augmentation transforms generate different results every time they are called.

Augmented image

Base class

RandomTransform

Bases: Transform

Base class for stochastic augmentation transforms.

Parameters:

Name Type Description Default
**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.

Composition

Transform Description
Compose Compose several transforms together
OneOf Apply one of the given transforms

Spatial

Transform Description
RandomFlip Randomly reverse the order of elements in an image along the given axes
RandomAffine Apply a random affine transformation
RandomElasticDeformation Apply a random elastic deformation
RandomAffineElasticDeformation Apply random affine and elastic deformation
RandomAnisotropy Downsample an image along an axis and upsample back

Intensity

Transform Description
RandomMotion Simulate MRI motion artifacts
RandomGhosting Simulate MRI ghosting artifacts
RandomSpike Simulate MRI spike artifacts
RandomBiasField Simulate MRI bias field artifacts
RandomBlur Blur an image using a random-sized Gaussian filter
RandomNoise Add Gaussian noise
RandomSwap Randomly swap patches in an image
RandomLabelsToImage Generate an image from a segmentation
RandomGamma Randomly change contrast of an image