wmhSegmentation: White matter hyperintensity probabilistic segmentation

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

White matter hyperintensity probabilistic segmentation

Description

Perform white matter hyperintensity probabilistic segmentation using deep learning

Usage

wmhSegmentation(
  flair,
  t1,
  whiteMatterMask = NULL,
  useCombinedModel = TRUE,
  predictionBatchSize = 16,
  patchStrideLength = 32,
  doPreprocessing = TRUE,
  antsxnetCacheDirectory = NULL,
  verbose = FALSE
)

Arguments

flair	input 3-D FLAIR brain image.
t1	input 3-D T1-weighted brain image (assumed to be aligned to the flair).
whiteMatterMask	input white matter mask for patch extraction. If None, calculated using deepAtropos (labels 3 and 4).
useCombinedModel	Original or combined.
predictionBatchSize	Control memory usage for prediction. More consequential for GPU-usage.
patchStrideLength	3-D vector or int. Dictates the stride length for accumulating predicting patches.
doPreprocessing	perform n4 bias correction, intensity truncation, brain extraction.
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 inst/extdata/ subfolder of the ANTsRNet package.
verbose	print progress.

Value

probabilistic image.

Author(s)

Tustison NJ

Examples

## Not run: 
library( ANTsRNet )
library( keras )

t1 <- antsImageRead( "t1.nii.gz" )
flair <- antsImageRead( "flair.nii.gz" )
results <- wmhSegmentation( t1, flair )

## End(Not run)

ANTsX/ANTsRNet documentation built on April 28, 2024, 12:16 p.m.