cerebellumMorphology: Cerebellum tissue segmentation, Schmahmann parcellation, and...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Cerebellum tissue segmentation, Schmahmann parcellation, and thickness.

Description

Perform cerebellum segmentation using a modification of the set of MaGET cerebellum atlases — https://www.cobralab.ca/cerebellum-lobules.

Usage

cerebellumMorphology(
  t1,
  cerebellumMask = NULL,
  computeThicknessImage = FALSE,
  doPreprocessing = TRUE,
  verbose = FALSE
)

Arguments

t1	raw or preprocessed 3-D T1-weighted whole head image.
cerebellumMask	Option for initialization. If not specified, the cerebellum ROI is determined using ANTsXNet brain_extraction followed by registration to a template.
computeThicknessImage	Compute KellyKapowski thickness image of the gray matter.
doPreprocessing	Perform N4 bias correction and spatiall normalize to template space.
verbose	print progress.

Details

https://www.nature.com/articles/s41598-024-59440-6

The tissue labeling is as follows:

• Label 1 :CSF

• Label 2 :Gray matter

• Label 3 :White matter

The parcellation labeling is as follows:

• Label 1 :L_I_II

• Label 2 :L_III

• Label 3 :L_IV

• Label 4 :L_V

• Label 5 :L_VI

• Label 6 :L_Crus_I

• Label 7 :L_Crus_II

• Label 8 :L_VIIB

• Label 9 :L_VIIIA

• Label 10 :L_VIIIB

• Label 11 :L_IX

• Label 12 :L_X

• Label 101:R_I_II

• Label 102:R_III

• Label 103:R_IV

• Label 104:R_V

• Label 105:R_VI

• Label 106:R_Crus_I

• Label 107:R_Crus_II

• Label 108:R_VIIB

• Label 109:R_VIIIA

• Label 110:R_VIIIB

• Label 111:R_IX

• Label 112:R_X

Value

List consisting of the multiple segmentation images and probability images for each label and foreground. Optional thickness image.

Author(s)

Tustison NJ, Tustison MG

Examples

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

image <- antsImageRead( "t1.nii.gz" )
cereb <- cerebellumMorphology( image )

## End(Not run)

ANTsX/ANTsRNet documentation built on Nov. 25, 2024, 10:27 p.m.