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

Author

Brian B. Avants

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 ) ) ) )
}