randomImageTransformAugmentation: Apply random transforms to a predictor / outcome training...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Apply random transforms to a predictor / outcome training image set

Description

The function will apply rigid, affine or deformable maps to an input set of training images. The reference image domain defines the space in which this happens.

Usage

randomImageTransformAugmentation(
  imageDomain,
  predictorImageList,
  outcomeImageList,
  n = 8,
  typeOfTransform = "Affine",
  interpolator = c("linear", "nearestNeighbor"),
  sdAffine = 1,
  nControlPoints = 100,
  spatialSmoothing = 3,
  composeToField = FALSE,
  numberOfCompositions = 4,
  deformationBasis,
  directoryName,
  imageDomainY,
  normalization = "none"
)

Arguments

imageDomain	defines default spatial domain for images. NOTE: if the input images do not match the spatial domain of the domain image, we internally resample the target to the domain. This may have unexpected consequences if you are not aware of this. This operation will test antsImagePhysicalSpaceConsistency then call resampleImageToTarget upon failure. The domain will, by default, be applied to outcome images if imageDomainY is not set.
predictorImageList	list of lists of image predictors
outcomeImageList	optional list of image outcomes
n	number of simulations to run
typeOfTransform	one of the following options c("Translation","Rigid","ScaleShear","Affine","Deformation", "AffineAndDeformation", "DeformationBasis")
interpolator	nearestNeighbor or linear (string) for predictor and outcome images respectively
sdAffine	roughly controls deviation from identity matrix
nControlPoints	number of control points for simulated deformation
spatialSmoothing	spatial smoothing for simulated deformation
composeToField	defaults to FALSE, will return deformation fields otherwise i.e. maps any transformation to a single deformation field.
numberOfCompositions	integer greater than or equal to one
deformationBasis	list containing deformationBasis set
directoryName	where to write to disk (optional)
imageDomainY	optional spatial domain for outcome images.
normalization	optional intensity normalization either none, standardize or 01.

Value

list (if no directory set) or boolean for success, failure

Author(s)

Avants BB

See Also

randomImageTransformBatchGenerator

Examples

library( ANTsR )
i1 = antsImageRead( getANTsRData( "r16" ) )
i2 = antsImageRead( getANTsRData( "r64" ) )
s1 = thresholdImage( i1, "Otsu", 3 )
s2 = thresholdImage( i2, "Otsu", 3 )
rand = randomImageTransformAugmentation( i1,
  list( list(i1), list(i2) ),  list( s1, s2 ) )

ANTsX/ANTsRNet documentation built on April 18, 2024, 8 a.m.