Six tissue segmentation

Perform Atropos-style six tissue segmentation using deep learning

deepAtropos(
  t1,
  doPreprocessing = TRUE,
  useSpatialPriors = 1,
  antsxnetCacheDirectory = NULL,
  verbose = FALSE,
  debug = FALSE
)

Arguments

t1	raw or preprocessed 3-D T1-weighted brain image.
doPreprocessing	perform preprocessing. See description above.
useSpatialPriors	Use MNI spatial tissue priors (0 or 1). Currently, only '0' (no priors) and '1' (cerebellar prior only) are the only two options. Default is 1.
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.
debug	return feature images in the last layer of the u-net model.

Value

list consisting of the segmentation image and probability images for each label.

Details

The labeling is as follows:

• Label 0:background

• Label 1:CSF

• Label 2:gray matter

• Label 3:white matter

• Label 4:deep gray matter

• Label 5:brain stem

• Label 6:cerebellum

Preprocessing on the training data consisted of:

• n4 bias correction,

• denoising,

• brain extraction, and

• affine registration to MNI. The input T1 should undergo the same steps. If the input T1 is the raw T1, these steps can be performed by the internal preprocessing, i.e. set doPreprocessing = TRUE

Author

Tustison NJ

Examples

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

image <- antsImageRead( "t1.nii.gz" )
results <- deepAtropos( image )
}