Longitudinal cortical thickness using deep learning

Perform KellyKapowski cortical thickness longitudinally using deepAtropos for segmentation of the derived single-subject template. It takes inspiration from the work described here:

longitudinalCorticalThickness(
  t1s,
  initialTemplate = "oasis",
  numberOfIterations = 1,
  refinementTransform = "antsRegistrationSyNQuick[a]",
  antsxnetCacheDirectory = NULL,
  verbose = FALSE
)

Arguments

t1s	input list of 3-D unprocessed T1-weighted brain images from a single subject
initialTemplate	input image to define the orientation of the SST. Can be a string (see getANTsXNetData) or a specified template. This allows the user to create a SST outside of this routine.
numberOfIterations	Defines the number of iterations for refining the SST.
refinementTransform	Transform for defining the refinement registration transform. See options in antsRegistration.
antsxnetCacheDirectory	destination directory for storing the downloaded template and model weights. Since these can be resused, if is.null(antsxnetCacheDirectory), these data will be downloaded to the subdirectory ~/.keras/ANTsXNet/.
verbose	print progress.

Value

List consisting of the SST, and a (sub-)list for each subject consisting of the preprocessed image, cortical thickness image, segmentation probability images, and affine mapping to the SST.

Details

https://pubmed.ncbi.nlm.nih.gov/31356207/

Author

Tustison NJ, Avants BB

Examples

if (FALSE) {
library( ANTsRNet )
library( keras )

image <- antsImageRead( "t1w_image.nii.gz" )
kk <- corticalThickness( image )
}