deepAtropos: Six tissue segmentation

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Six tissue segmentation

Description

Perform Atropos-style six tissue segmentation using deep learning

Usage

deepAtropos(
  t1,
  doPreprocessing = TRUE,
  useSpatialPriors = 1,
  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.
verbose	print progress.
debug	return feature images in the last layer of the u-net model.

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

Value

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

Author(s)

Tustison NJ

Examples

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

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

## End(Not run)

ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.