Propagate Joint Label Fusion (JLF) solutions through a time series

The JLF initialization is reused throughout a time series with an additional lag argument.

jlfProp(
  targetI,
  targetIMask,
  atlasList,
  rad = 2,
  labelList = NULL,
  rSearch = 3,
  lagValue = 3,
  verbose = FALSE,
  ...
)

Arguments

targetI	antsImage list to be approximated
targetIMask	mask with value 1
atlasList	list containing antsImages with intensity images
rad	neighborhood radius, default to 2
labelList	optional list containing antsImages with segmentation labels
rSearch	radius of search, default is 3
lagValue	number of prior images to use to fwd propagate JLF solution
verbose	boolean
...	arguments to pass to jointLabelFusion

Value

segmentation of time series

Examples

if (FALSE) {
set.seed(123)
ref = ri( 1 ) %>% resampleImage(  4  ) %>% iMath( "Normalize" )
mi1 = ri( 2 )
mi2 = ri( 3 )
mi3 = ri( 4 )
mi4 = ri( 5 )
mi5 = ri( 6 )
refmask<-getMask( ref )
refmask<-iMath( refmask, "MD", 10 ) # just to speed things up
ilist<-list( mi1, 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+1
 }
 tarlist = list(
   iMath( ref, "GD", 3),
   iMath( ref, "GD", 2),
   iMath( ref, "GD", 1),
   iMath( ref, "GD", 0),
   iMath( ref, "GE", 1),
   iMath( ref, "GE", 2),
   iMath( ref, "GE", 3) )
  pp = jlfProp( tarlist, refmask, ilist, rSearch=2,
  labelList=seglist, rad = rep( 2, length( dim( ref ) ) ) )
}