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mrvnrfs.R
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mrvnrfs.R
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#' multi-res voxelwise neighborhood random forest segmentation learning
#'
#' Represents multiscale feature images as a neighborhood and uses the features
#' to build a random forest segmentation model from an image population
#'
#' @param y list of training labels. either an image or numeric value
#' @param x a list of lists where each list contains feature images
#' @param labelmask a mask for the features (all in the same image space)
#' the labelmask defines the number of parallel samples that will be used
#' per subject sample. two labels will double the number of predictors
#' contributed from each feature image.
#' @param rad vector of dimensionality d define nhood radius
#' @param nsamples (per subject to enter training)
#' @param ntrees (for the random forest model)
#' @param multiResSchedule an integer vector defining multi-res levels
#' @param asFactors boolean - treat the y entries as factors
#' @return list a 4-list with the rf model, training vector, feature matrix
#' and the random mask
#' @author Avants BB, Tustison NJ, Pustina D
#'
#' @examples
#'
#' mask<-makeImage( c(10,10), 0 )
#' mask[ 3:6, 3:6 ]<-1
#' mask[ 5, 5:6]<-2
#' ilist<-list()
#' lablist<-list()
#' inds<-1:50
#' scl<-0.33 # a noise parameter
#' for ( predtype in c("label","scalar") )
#' {
#' for ( i in inds ) {
#' img<-antsImageClone(mask)
#' imgb<-antsImageClone(mask)
#' limg<-antsImageClone(mask)
#' if ( predtype == "label") { # 4 class prediction
#' img[ 3:6, 3:6 ]<-rnorm(16)*scl+(i %% 4)+scl*mean(rnorm(1))
#' imgb[ 3:6, 3:6 ]<-rnorm(16)*scl+(i %% 4)+scl*mean(rnorm(1))
#' limg[ 3:6, 3:6 ]<-(i %% 4)+1 # the label image is constant
#' }
#' if ( predtype == "scalar") {
#' img[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1))
#' imgb[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1))
#' limg<-i^2.0 # a real outcome
#' }
#' ilist[[i]]<-list(img,imgb) # two features
#' lablist[[i]]<-limg
#' }
#' rad<-rep( 1, 2 )
#' mr <- c(1.5,1)
#' rfm<-mrvnrfs( lablist , ilist, mask, rad=rad, multiResSchedule=mr,
#' asFactors = ( predtype == "label" ) )
#' rfmresult<-mrvnrfs.predict( rfm$rflist,
#' ilist, mask, rad=rad, asFactors=( predtype == "label" ),
#' multiResSchedule=mr )
#' if ( predtype == "scalar" )
#' print( cor( unlist(lablist) , rfmresult$seg ) )
#' } # end predtype loop
#'
#'
#' @export mrvnrfs
mrvnrfs <- function( y, x, labelmasks, rad=NA, nsamples=1,
ntrees=500, multiResSchedule=c(4,2,1), asFactors=TRUE,
voxchunk=50000) {
# check if Y is antsImage or a number
yisimg<-TRUE
if ( typeof(y[[1]]) == "integer" | typeof(y[[1]]) == "double") yisimg<-FALSE
rflist<-list()
rfct<-1
# for a single labelmask create a list with it
useFirstMask=FALSE
if ( typeof(labelmasks) != "list" ) {
inmask = antsImageClone( labelmasks )
labelmasks=list()
for ( i in 1:length(x) ) labelmasks[[i]] = inmask
useFirstMask = TRUE
}
# loop of resolutions
mrcount=0
for ( mr in multiResSchedule ) {
mrcount=mrcount+1
message(paste(mrcount,'of',length(multiResSchedule)))
invisible(gc())
# add newprobs from previous run, already correct dimension
if ( rfct > 1 ) {
for ( kk in 1:length(x) ) {
p1<-unlist( x[[kk]] )
p2<-unlist(newprobs[[kk]])
temp<-lappend( p1 , p2 )
x[[kk]]<-temp
}
rm(newprobs); invisible(gc())
}
invisible(gc())
# build model for this mr
if (!useFirstMask) sol<-vwnrfs( y, x, labelmasks, rad, nsamples, ntrees, asFactors, reduceFactor = mr )
if (useFirstMask) sol<-vwnrfs( y, x, labelmasks[[1]], rad, nsamples, ntrees, asFactors, reduceFactor = mr )
sol$fm = sol$tv = sol$randmask = NULL
invisible(gc())
# if not last mr, predict new features for next round
if (mrcount < length(multiResSchedule)) {
predme = vwnrfs.predict(rfm=sol$rfm, x=x, labelmasks=labelmasks,
rad=rad, asFactors=TRUE, voxchunk=voxchunk,
reduceFactor = mr)
newprobs=predme$probs
rm(predme); invisible(gc())
for ( tt1 in 1:length(newprobs) )
for (tt2 in 1:length(newprobs[[tt1]]))
newprobs[[tt1]][[tt2]]<-resampleImage( newprobs[[tt1]][[tt2]], dim(labelmasks[[tt1]]), useVoxels=1, 0 )
}
invisible(gc())
rflist[[rfct]]<-sol$rfm
rfct<-rfct+1
} # mr loop
return( list(rflist=rflist) )
}
#' multi-res voxelwise neighborhood random forest segmentation
#'
#' Represents multiscale feature images as a neighborhood and uses the features
#' to apply a random forest segmentation model to a new image
#'
#' @param rflist a list of random forest models from mrvnrfs
#' @param x a list of lists where each list contains feature images
#' @param labelmask a mask for the features (all in the same image space)
#' @param rad vector of dimensionality d define nhood radius
#' @param multiResSchedule an integer vector defining multi-res levels
#' @param asFactors boolean - treat the y entries as factors
#' @return list a 4-list with the rf model, training vector, feature matrix
#' and the random mask
#' @author Avants BB, Tustison NJ, Pustina D
#'
#' @export mrvnrfs.predict
mrvnrfs.predict <- function( rflist, x, labelmasks, rad=NA,
multiResSchedule=c(4,2,1), asFactors=TRUE,
voxchunk=60000) {
if ( ! usePkg("randomForest") )
stop("Please install the randomForest package, example: install.packages('randomForest')")
# for a single labelmask create a list the same
useFirstMask=FALSE
if ( typeof(labelmasks) != "list" ) {
inmask = antsImageClone( labelmasks )
labelmasks=list()
for ( i in 1:length(x) ) labelmasks[[i]] = inmask
useFirstMask = TRUE
}
predtype<-'response'
if ( asFactors ) predtype<-'prob'
rfct<-1
for ( mr in multiResSchedule ){
if ( rfct > 1 ) {
for ( kk in 1:length(x) ) {
p1<-unlist( x[[kk]] )
p2<-unlist(newprobs[[kk]])
temp<-lappend( p1 , p2 )
x[[kk]]<-temp
}
rm(newprobs); invisible(gc())
}
predme = vwnrfs.predict(rflist[[rfct]], x=x, labelmasks=labelmasks,
rad=rad, asFactors=TRUE, voxchunk=voxchunk,
reduceFactor = mr)
newprobs = predme$probs
newseg = predme$seg
if (rfct < length(multiResSchedule)) {
for ( tt1 in 1:length(newprobs) )
for (tt2 in 1:length(newprobs[[tt1]]))
newprobs[[tt1]][[tt2]]<-resampleImage( newprobs[[tt1]][[tt2]], dim(labelmasks[[1]]), useVoxels=1, 0 )
}
rfct<-rfct+1
} # mr loop
return(list(seg=newseg, probs=newprobs))
}