uvaSeg: Unsupervised variational autoencoder segmentation

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Unsupervised variational autoencoder segmentation

Description

Trains a variational autoencoding with a convolutional network. This is followed by k-means clustering to produce a segmentation and probabilities.

Usage

uvaSeg(
  image,
  model,
  k,
  mask,
  returnProbabilities = FALSE,
  batchSize = 1028,
  standardize = TRUE,
  verbose = FALSE
)

Arguments

image	input image
model	the model output from uvaSegTrain
k	number of clusters or cluster centers
mask	defining output segmentation space
returnProbabilities	boolean
batchSize	for the prediction
standardize	boolean controlling whether patches are standardized
verbose	boolean

Value

segmentation and probability images are output

Author(s)

Avants BB

Examples

## Not run: 
library(ANTsR)
img <- ri( 1 )  %>% resampleImage( c(4,4) )  %>% iMath( "Normalize" )
mask = randomMask( getMask( img ), 50 )
patch = getNeighborhoodInMask( img, mask, c(3,3), boundary.condition = "NA" )
uvaSegModel = uvaSegTrain( patch, k = 6 )
tarImg = ri( 3 ) %>% resampleImage( c(4,4) )
uvaSegmentation = uvaSeg(tarImg, uvaSegModel, k = 3, getMask( tarImg ) )

## End(Not run)

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