jointLabelFusion: joint label and intensity fusion

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

joint label and intensity fusion

Description

A multiple atlas voting scheme to customize labels for a new subject. This function will also perform intensity fusion. It almost directly calls the C++ in the ANTs executable so is much faster than other variants in ANTsR. One may want to normalize image intensities for each input image before passing to this function. If no labels are passed, we do intensity fusion. Note on computation time: the underlying C++ is multithreaded. You can control the number of threads by setting the environment variable ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS e.g. to use all or some of your CPUs. This will improve performance substantially. For instance, on a macbook pro from 2015, 8 cores improves speed by about 4x.

Usage

jointLabelFusion(
  targetI,
  targetIMask,
  atlasList,
  beta = 4,
  rad = 2,
  labelList = NULL,
  rho = 0.01,
  usecor = FALSE,
  rSearch = 3,
  nonnegative = FALSE,
  maxLabelPlusOne = FALSE,
  noZeroes = FALSE,
  verbose = FALSE
)

Arguments

targetI	antsImage to be approximated
targetIMask	mask with value 1
atlasList	list containing antsImages with intensity images
beta	weight sharpness, default to 2
rad	neighborhood radius, default to 2
labelList	optional list containing antsImages with segmentation labels
rho	ridge penalty increases robustness to outliers but also makes image converge to average
usecor	employ correlation as local similarity
rSearch	radius of search, default is 3
nonnegative	constrain weights to be non-negative
maxLabelPlusOne	boolean this will add max label plus one to the non-zero parts of each label where the target mask is greater than one. NOTE: this will have a side effect of adding to the original label images that are passed to the program. It also guarantees that every position in the labels have some label, rather than none. Ie it guarantees to explicitly parcellate the input data.
noZeroes	boolean will zero out target mask regions that have any zero label. this prevents JLF from computing a solution in regions not covered by the initial library.
verbose	boolean

Value

approximated image, segmentation and probabilities

Author(s)

Brian B. Avants, Hongzhi Wang, Paul Yushkevich, Nicholas J. Tustison

Examples

set.seed(123)
ref <- ri(1)
ref <- resampleImage(ref, c(50, 50), 1, 0)
ref <- iMath(ref, "Normalize")
mi <- ri(2)
mi2 <- ri(3)
mi3 <- ri(4)
mi4 <- ri(5)
mi5 <- ri(6)
refmask <- getMask(ref)
refmask <- iMath(refmask, "ME", 2) # just to speed things up
ilist <- list(mi, mi2, mi3, mi4, mi5)
seglist <- list()
for (i in 1:length(ilist))
{
  ilist[[i]] <- iMath(ilist[[i]], "Normalize")
  mytx <- antsRegistration(
    fixed = ref, moving = ilist[[i]],
    typeofTransform = c("Affine"), verbose = TRUE
  )
  mywarpedimage <- antsApplyTransforms(
    fixed = ref,
    moving = ilist[[i]],
    transformlist = mytx$fwdtransforms
  )
  ilist[[i]] <- mywarpedimage
  seg <- thresholdImage(ilist[[i]], "Otsu", 3)
  seglist[[i]] <- seg
}
r <- 2
pp <- jointLabelFusion(ref, refmask, ilist,
  rSearch = 2,
  labelList = seglist, rad = rep(r, length(dim(ref)))
)
pp2 <- jointLabelFusion(ref, refmask, ilist,
  rSearch = 2,
  labelList = seglist, rad = rep(r, length(dim(ref)))
)
testthat::expect_equal(pp2$segmentation, pp$segmentation)
pp <- jointLabelFusion(ref, refmask, ilist,
  rSearch = 2,
  rad = rep(r, length(dim(ref)))
)

## Not run: 
ref <- antsImageRead(getANTsRData("ch2"))
n <- 50
ref <- resampleImage(ref, c(n, n, n), 1, 0)
ref <- iMath(ref, "Normalize")
refmask <- getMask(ref)
ilist <- list()
seglist <- list()
for (k in 1:5) {
  mi <- antsImageClone(ref) + rnorm(n^3, 0, 0.1)
  mykseg <- kmeansSegmentation(mi, 3, refmask)$segmentation
  ilist[[k]] <- mi
  seglist[[k]] <- mykseg
}
pp <- jointLabelFusion(ref, refmask, ilist,
  rSearch = 2,
  labelList = seglist, rad = rep(2, length(dim(ref))), verbose = TRUE
)
plot(ref, pp$segmentation)
plot(pp$intensity)

## End(Not run)

stnava/ANTsR documentation built on April 16, 2024, 12:17 a.m.