brainTumorSegmentation: Brain tumor probabilistic segmentation

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Brain tumor probabilistic segmentation

Description

Perform brain tumor probabilistic segmentation given pre-aligned FLAIR, T1, T1 contrast, and T2 images. Note that the underlying model is 3-D and requires images to be of > 64 voxels in each dimension.

Usage

brainTumorSegmentation(
  flair,
  t1,
  t1Contrast,
  t2,
  predictionBatchSize = 16,
  patchStrideLength = 32,
  doPreprocessing = TRUE,
  antsxnetCacheDirectory = NULL,
  verbose = FALSE
)

Arguments

flair	input 3-D FLAIR brain image (not skull-stripped).
t1	input 3-D T1-weighted brain image (not skull-stripped).
t1Contrast	input 3-D T1-weighted contrast brain image (not skull-stripped).
t2	input 3-D T2-weighted brain image (not skull-stripped).
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

Brain tumor segmentation probability images (4 tumor tissue types).

Author(s)

Tustison NJ

Examples

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

t1 <- antsImageRead( "t1.nii.gz" )
flair <- antsImageRead( "flair.nii.gz" )

## End(Not run)

ANTsX/ANTsRNet documentation built on April 23, 2024, 1:24 p.m.