R/mouse.R
In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing
Defines functions
mouseCorticalThickness
mouseBrainParcellation
mouseBrainExtraction
Documented in
mouseBrainExtraction
mouseBrainParcellation
mouseCorticalThickness
#' Mouse brain extraction
#'
#' Perform brain extraction of mouse MRI
#'
#' @param image input 3-D brain image (or list of images for multi-modal scenarios).
#' @param modality image type. Options include:
#' \itemize{
#' \item{"t2": }{T2-weighted MRI}
#' \item{"ex5coronal": }{E13.5 and E15.5 mouse embroyonic histology data.}
#' \item{"ex5sagittal": }{E13.5 and E15.5 mouse embroyonic histology data.}
#' }
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param whichAxis Specify direction for ex5 modalities..
#' @param verbose print progress.
#' @return brain probability mask
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "brain.nii.gz" )
#' probabilityMask <- mouseBrainExtraction( image, modality = "t2" )
#' }
#' @export
mouseBrainExtraction <- function( image,
modality = c( "t2", "ex5coronal", "ex5sagittal" ),
returnIsotropicOutput = FALSE, whichAxis = 2,
verbose = FALSE )
{
if( whichAxis < 1 || whichAxis > 3 )
{
stop( "Chosen axis not supported." )
}
if( modality == "t2" )
{
templateShape <- c( 176, 176, 176 )
template <- antsImageRead( getANTsXNetData( "bsplineT2MouseTemplate" ) )
template <- resampleImage( template, templateShape, useVoxels = TRUE, interpType = 0 )
templateMask <- antsImageRead( getANTsXNetData( "bsplineT2MouseTemplateBrainMask" ) )
templateMask <- resampleImage( template, templateShape, useVoxels = TRUE, interpType = 1 )
if( verbose )
{
message( "Preprocessing: Warping to B-spline T2w mouse template." )
}
centerOfMassReference <- getCenterOfMass( templateMask )
centerOfMassImage <- getCenterOfMass( image )
translation <- as.array( centerOfMassImage ) - as.array( centerOfMassReference )
xfrm <- createAntsrTransform( type = "Euler3DTransform",
center = centerOfMassReference,
translation = centerOfMassImage - centerOfMassReference )
imageWarped <- applyAntsrTransformToImage( xfrm, image, templateMask, interpolation = "linear" )
imageWarped <- iMath( imageWarped, "Normalize" )
unetModel <- createUnetModel3D( c( templateShape, 1 ),
numberOfOutputs = 1, mode = "sigmoid",
numberOfFilters = c( 16, 32, 64, 128 ),
convolutionKernelSize = 3, deconvolutionKernelSize = 2 )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainExtraction3D" )
unetModel$load_weights( weightsFileName )
batchX <- array( data = 0, dim = c( 1, templateShape, 1 ) )
batchX[1,,,,1] = as.array( imageWarped )
if( verbose )
{
message( "Prediction." )
}
predictedData <- drop( unetModel$predict( batchX, verbose = verbose ) )
predictedImage <- as.antsImage( predictedData, reference = template )
probabilityMask <- applyAntsrTransformToImage( invertAntsrTransform( xfrm ),
predictedImage, image )
return( probabilityMask )
}
}
#' Mouse brain parcellation
#'
#' Perform brain extraction of mouse T2 MRI
#'
#' @param image input 3-D brain image (or list of images for multi-modal scenarios).
#' @param mask Brain mask. If not specified, one is estimated using ANTsXNet mouse
#' brain extraction.
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param whichParcellation Brain parcellation type:
#' \itemize{
#' \item{"nick":
#' \itemize{
#' \item{Label 0:}{background}
#' \item{Label 1:}{cerebral cortex}
#' \item{Label 2:}{cerebral nuclei}
#' \item{Label 3:}{brain stem}
#' \item{Label 4:}{cerebellum}
#' \item{Label 5:}{main olfactory bulb}
#' \item{Label 6:}{hippocampal formation}
#' }}
#' }
#' \itemize{
#' \item{"tct":
#' \itemize{
#' \item{Label 0:}{}
#' \item{Label 1:}{background}
#' \item{Label 2:}{Infralimbic area}
#' \item{Label 3:}{Prelimbic area}
#' \item{Label 4:}{Medial group of the dorsal thalamus}
#' \item{Label 5:}{Reticular nucleus of the thalamus}
#' \item{Label 6:}{Hippocampal formation}
#' \item{Label 7:}{Cerebellum}
#' }}
#' }
#' \itemize{
#' \item{"jay":
#' \itemize{
#' \item{Label 0:}{background}
#' \item{Label 1:}{}
#' \item{Label 2:}{}
#' \item{Label 3:}{}
#' \item{Label 4:}{}
#' }}
#' }
#' @param verbose print progress.
#' @return brain probability mask
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "brain.nii.gz" )
#' parcellation <- mouseBrainParcellation( image, modality = "t2" )
#' }
#' @export
mouseBrainParcellation <- function( image,
mask = NULL, returnIsotropicOutput = FALSE,
whichParcellation = c( "nick", "jay", "tct" ),
verbose = FALSE )
{
if( whichParcellation == "nick" || whichParcellation == "tct" || whichParcellation == "jay" )
{
templateSpacing <- c( 0.075, 0.075, 0.075 )
templateCropSize <- c( 176, 176, 176 )
if( whichParcellation == "nick" )
{
templateString <- "DevCCF P56 T2w"
template <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um" ) )
templateMatch <- rankIntensity( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um_BrainParcellationNickMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainParcellation3DNick" )
} else if( whichParcellation == "tct" ) {
templateString <- "DevCCF P56 T2w"
template <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um" ) )
templateMatch <- rankIntensity( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um_BrainParcellationTctMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainParcellation3DTct" )
} else if( whichParcellation == "jay" ) {
templateString <- "DevCCF P04 STPT"
template <- antsImageRead( getANTsXNetData( "DevCCF_P04_STPT_50um" ) )
templateMatch <- histogramEqualizeImage( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P04_STPT_50um_BrainParcellationJayMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseSTPTBrainParcellation3DJay" )
}
templateMatch <- (( templateMatch - ANTsR::min( templateMatch ) ) /
( ANTsR::max( templateMatch ) - ANTsR::min( templateMatch ) ))
antsSetSpacing( template, c( 0.05, 0.05, 0.05 ) )
template <- resampleImage( template, templateSpacing, useVoxels = FALSE, interpType = 4 )
template <- padOrCropImageToSize( template, templateCropSize )
antsSetSpacing( templateMask, c( 0.05, 0.05, 0.05 ) )
templateMask <- resampleImage( templateMask, templateSpacing, useVoxels = FALSE, interpType = 1 )
templateMask <- padOrCropImageToSize( templateMask, templateCropSize )
numberOfNonzeroLabels <- length( sort( unique( as.vector( as.array( templateMask ) ) ) ) ) - 1
templatePriors <- list()
for( i in seq.int( numberOfNonzeroLabels ) )
{
singleLabel <- thresholdImage( templateMask, i, i, 1, 0 )
prior <- smoothImage( singleLabel, sigma = 0.003, sigmaInPhysicalCoordinates = TRUE )
templatePriors[[i]] <- prior
}
if( is.null( mask ) )
{
if( verbose )
{
message( "Preprocessing: Brain extraction." )
}
mask <- mouseBrainExtraction( image, modality = "t2",
verbose = verbose )
mask <- thresholdImage( mask, 0.5, 1.1, 1, 0 )
mask <- labelClusters( mask, fullyConnected = TRUE )
mask <- thresholdImage( mask, 1, 1, 1, 0 )
}
imageBrain <- image * mask
if ( verbose )
{
message( paste0( "Preprocessing: Warping to ", templateString, " mouse template." ) )
}
reg <- antsRegistration( template, imageBrain,
typeofTransform = "antsRegistrationSyNQuickRepro[a]",
verbose = verbose )
imageWarped <- NULL
if ( whichParcellation == "nick" || whichParcellation == "tct" )
{
imageWarped <- rankIntensity( reg$warpedmovout )
} else {
imageWarped <- antsImageClone( reg$warpedmovout )
}
imageWarped <- histogramMatchImage( imageWarped, templateMatch )
imageWarped <- iMath( imageWarped, "Normalize" )
numberOfFilters <- c( 16, 32, 64, 128, 256 )
numberOfClassificationLabels <- numberOfNonzeroLabels + 1
channelSize = 1 + numberOfNonzeroLabels
unetModel <- createUnetModel3D( c( dim( template ), channelSize ),
numberOfOutputs = numberOfClassificationLabels, mode = "classification",
numberOfFilters = numberOfFilters,
convolutionKernelSize = 3, deconvolutionKernelSize = 2 )
unetModel$load_weights( weightsFileName )
batchX <- array( data = 0, dim = c( 1, dim( template ), channelSize ) )
batchX[1,,,,1] = as.array( imageWarped )
for( i in seq.int( length( templatePriors ) ) )
{
batchX[1,,,,i+1] <- as.array( templatePriors[[i]] )
}
if( verbose )
{
message( "Prediction." )
}
predictedData <- drop( unetModel$predict( batchX, verbose = verbose ) )
referenceImage <- image
if( returnIsotropicOutput )
{
newSpacing <- rep( min( antsGetSpacing( image ) ), 3 )
referenceImage <- resampleImage( image, newSpacing, useVoxels = FALSE, interpType = 0 )
}
probabilityImages <- list()
for( i in seq.int( numberOfClassificationLabels ) )
{
if( verbose )
{
message( "Reconstructing image ", i, "\n" )
}
probabilityImage <- as.antsImage( predictedData[,,,i], reference = template )
probabilityImages[[i]] <- antsApplyTransforms( fixed = referenceImage,
moving = probabilityImage, transformlist = reg$invtransforms,
whichtoinvert = c( TRUE ), interpolator = "linear", verbose = verbose )
}
imageMatrix <- imageListToMatrix( probabilityImages, referenceImage * 0 + 1 )
segmentationMatrix <- matrix( apply( imageMatrix, 2, which.max ), nrow = 1 )
segmentationImage <- matrixToImages( segmentationMatrix, referenceImage * 0 + 1 )[[1]] - 1
results <- list( segmentationImage = segmentationImage,
probabilityImages = probabilityImages )
return( results )
} else {
stop( "Unrecognized parcellation." )
}
}
#' Mouse brain cortical thickness using deep learning
#'
#' Perform KellyKapowski cortical thickness
#'
#' @param t2 input 3-D unprocessed T2-weighted mouse brain image
#' @param mask Brain mask. If not specified, one is estimated using ANTsXNet mouse brain
#' extraction.
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param verbose print progress.
#' @return Cortical thickness image and segmentation probability images.
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "t1w_image.nii.gz" )
#' kk <- corticalThickness( image )
#' }
#' @export
mouseCorticalThickness <- function( t2, mask = NULL,
returnIsotropicOutput = FALSE,
verbose = FALSE )
{
parcellation <- mouseBrainParcellation( t2, mask = mask, whichParcellation = "nick",
returnIsotropicOutput = TRUE,
verbose = verbose )
# Kelly Kapowski cortical thickness
kkSegmentation <- antsImageClone(parcellation$segmentationImage)
kkSegmentation[kkSegmentation == 2] <- 3
kkSegmentation[kkSegmentation == 1] <- 2
kkSegmentation[kkSegmentation == 6] <- 2
corticalMatter <- parcellation$probabilityImages[[2]] + parcellation$probabilityImages[[7]]
otherMatter <- parcellation$probabilityImages[[3]] + parcellation$probabilityImages[[4]]
kk <- kellyKapowski( s = kkSegmentation, g = corticalMatter, w = otherMatter,
its = 45, r = 0.0025, m = 1.5, x = 0, t = 10, verbose = verbose )
if( ! returnIsotropicOutput )
{
kk <- resampleImage( kk, antsGetSpacing( t2 ), useVoxels = FALSE, interpType = 0 )
parcellation$segmentationImage <- resampleImage( parcellation$segmentationImage,
antsGetSpacing( t2 ), useVoxels = FALSE,
interpType = 1 )
for( i in seq.int( length( parcellation$probabilityImages ) ) )
{
parcellation$probabilityImages[[i]] <- resampleImage( parcellation$probabilityImages[[i]],
antsGetSpacing( t2 ), useVoxels = FALSE,
interpType = 0 )
}
}
return( list(
thicknessImage = kk,
parcellation = parcellation
) )
}
ANTsX/ANTsRNet documentation built on June 11, 2025, 1:45 p.m.
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Defines functions mouseCorticalThickness mouseBrainParcellation mouseBrainExtraction
Documented in mouseBrainExtraction mouseBrainParcellation mouseCorticalThickness
#' Mouse brain extraction
#'
#' Perform brain extraction of mouse MRI
#'
#' @param image input 3-D brain image (or list of images for multi-modal scenarios).
#' @param modality image type. Options include:
#' \itemize{
#' \item{"t2": }{T2-weighted MRI}
#' \item{"ex5coronal": }{E13.5 and E15.5 mouse embroyonic histology data.}
#' \item{"ex5sagittal": }{E13.5 and E15.5 mouse embroyonic histology data.}
#' }
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param whichAxis Specify direction for ex5 modalities..
#' @param verbose print progress.
#' @return brain probability mask
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "brain.nii.gz" )
#' probabilityMask <- mouseBrainExtraction( image, modality = "t2" )
#' }
#' @export
mouseBrainExtraction <- function( image,
modality = c( "t2", "ex5coronal", "ex5sagittal" ),
returnIsotropicOutput = FALSE, whichAxis = 2,
verbose = FALSE )
{
if( whichAxis < 1 || whichAxis > 3 )
{
stop( "Chosen axis not supported." )
}
if( modality == "t2" )
{
templateShape <- c( 176, 176, 176 )
template <- antsImageRead( getANTsXNetData( "bsplineT2MouseTemplate" ) )
template <- resampleImage( template, templateShape, useVoxels = TRUE, interpType = 0 )
templateMask <- antsImageRead( getANTsXNetData( "bsplineT2MouseTemplateBrainMask" ) )
templateMask <- resampleImage( template, templateShape, useVoxels = TRUE, interpType = 1 )
if( verbose )
{
message( "Preprocessing: Warping to B-spline T2w mouse template." )
}
centerOfMassReference <- getCenterOfMass( templateMask )
centerOfMassImage <- getCenterOfMass( image )
translation <- as.array( centerOfMassImage ) - as.array( centerOfMassReference )
xfrm <- createAntsrTransform( type = "Euler3DTransform",
center = centerOfMassReference,
translation = centerOfMassImage - centerOfMassReference )
imageWarped <- applyAntsrTransformToImage( xfrm, image, templateMask, interpolation = "linear" )
imageWarped <- iMath( imageWarped, "Normalize" )
unetModel <- createUnetModel3D( c( templateShape, 1 ),
numberOfOutputs = 1, mode = "sigmoid",
numberOfFilters = c( 16, 32, 64, 128 ),
convolutionKernelSize = 3, deconvolutionKernelSize = 2 )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainExtraction3D" )
unetModel$load_weights( weightsFileName )
batchX <- array( data = 0, dim = c( 1, templateShape, 1 ) )
batchX[1,,,,1] = as.array( imageWarped )
if( verbose )
{
message( "Prediction." )
}
predictedData <- drop( unetModel$predict( batchX, verbose = verbose ) )
predictedImage <- as.antsImage( predictedData, reference = template )
probabilityMask <- applyAntsrTransformToImage( invertAntsrTransform( xfrm ),
predictedImage, image )
return( probabilityMask )
}
}
#' Mouse brain parcellation
#'
#' Perform brain extraction of mouse T2 MRI
#'
#' @param image input 3-D brain image (or list of images for multi-modal scenarios).
#' @param mask Brain mask. If not specified, one is estimated using ANTsXNet mouse
#' brain extraction.
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param whichParcellation Brain parcellation type:
#' \itemize{
#' \item{"nick":
#' \itemize{
#' \item{Label 0:}{background}
#' \item{Label 1:}{cerebral cortex}
#' \item{Label 2:}{cerebral nuclei}
#' \item{Label 3:}{brain stem}
#' \item{Label 4:}{cerebellum}
#' \item{Label 5:}{main olfactory bulb}
#' \item{Label 6:}{hippocampal formation}
#' }}
#' }
#' \itemize{
#' \item{"tct":
#' \itemize{
#' \item{Label 0:}{}
#' \item{Label 1:}{background}
#' \item{Label 2:}{Infralimbic area}
#' \item{Label 3:}{Prelimbic area}
#' \item{Label 4:}{Medial group of the dorsal thalamus}
#' \item{Label 5:}{Reticular nucleus of the thalamus}
#' \item{Label 6:}{Hippocampal formation}
#' \item{Label 7:}{Cerebellum}
#' }}
#' }
#' \itemize{
#' \item{"jay":
#' \itemize{
#' \item{Label 0:}{background}
#' \item{Label 1:}{}
#' \item{Label 2:}{}
#' \item{Label 3:}{}
#' \item{Label 4:}{}
#' }}
#' }
#' @param verbose print progress.
#' @return brain probability mask
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "brain.nii.gz" )
#' parcellation <- mouseBrainParcellation( image, modality = "t2" )
#' }
#' @export
mouseBrainParcellation <- function( image,
mask = NULL, returnIsotropicOutput = FALSE,
whichParcellation = c( "nick", "jay", "tct" ),
verbose = FALSE )
{
if( whichParcellation == "nick" || whichParcellation == "tct" || whichParcellation == "jay" )
{
templateSpacing <- c( 0.075, 0.075, 0.075 )
templateCropSize <- c( 176, 176, 176 )
if( whichParcellation == "nick" )
{
templateString <- "DevCCF P56 T2w"
template <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um" ) )
templateMatch <- rankIntensity( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um_BrainParcellationNickMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainParcellation3DNick" )
} else if( whichParcellation == "tct" ) {
templateString <- "DevCCF P56 T2w"
template <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um" ) )
templateMatch <- rankIntensity( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P56_MRI_T2_50um_BrainParcellationTctMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseT2wBrainParcellation3DTct" )
} else if( whichParcellation == "jay" ) {
templateString <- "DevCCF P04 STPT"
template <- antsImageRead( getANTsXNetData( "DevCCF_P04_STPT_50um" ) )
templateMatch <- histogramEqualizeImage( template )
templateMask <- antsImageRead( getANTsXNetData( "DevCCF_P04_STPT_50um_BrainParcellationJayMask" ) )
weightsFileName <- getPretrainedNetwork( "mouseSTPTBrainParcellation3DJay" )
}
templateMatch <- (( templateMatch - ANTsR::min( templateMatch ) ) /
( ANTsR::max( templateMatch ) - ANTsR::min( templateMatch ) ))
antsSetSpacing( template, c( 0.05, 0.05, 0.05 ) )
template <- resampleImage( template, templateSpacing, useVoxels = FALSE, interpType = 4 )
template <- padOrCropImageToSize( template, templateCropSize )
antsSetSpacing( templateMask, c( 0.05, 0.05, 0.05 ) )
templateMask <- resampleImage( templateMask, templateSpacing, useVoxels = FALSE, interpType = 1 )
templateMask <- padOrCropImageToSize( templateMask, templateCropSize )
numberOfNonzeroLabels <- length( sort( unique( as.vector( as.array( templateMask ) ) ) ) ) - 1
templatePriors <- list()
for( i in seq.int( numberOfNonzeroLabels ) )
{
singleLabel <- thresholdImage( templateMask, i, i, 1, 0 )
prior <- smoothImage( singleLabel, sigma = 0.003, sigmaInPhysicalCoordinates = TRUE )
templatePriors[[i]] <- prior
}
if( is.null( mask ) )
{
if( verbose )
{
message( "Preprocessing: Brain extraction." )
}
mask <- mouseBrainExtraction( image, modality = "t2",
verbose = verbose )
mask <- thresholdImage( mask, 0.5, 1.1, 1, 0 )
mask <- labelClusters( mask, fullyConnected = TRUE )
mask <- thresholdImage( mask, 1, 1, 1, 0 )
}
imageBrain <- image * mask
if ( verbose )
{
message( paste0( "Preprocessing: Warping to ", templateString, " mouse template." ) )
}
reg <- antsRegistration( template, imageBrain,
typeofTransform = "antsRegistrationSyNQuickRepro[a]",
verbose = verbose )
imageWarped <- NULL
if ( whichParcellation == "nick" || whichParcellation == "tct" )
{
imageWarped <- rankIntensity( reg$warpedmovout )
} else {
imageWarped <- antsImageClone( reg$warpedmovout )
}
imageWarped <- histogramMatchImage( imageWarped, templateMatch )
imageWarped <- iMath( imageWarped, "Normalize" )
numberOfFilters <- c( 16, 32, 64, 128, 256 )
numberOfClassificationLabels <- numberOfNonzeroLabels + 1
channelSize = 1 + numberOfNonzeroLabels
unetModel <- createUnetModel3D( c( dim( template ), channelSize ),
numberOfOutputs = numberOfClassificationLabels, mode = "classification",
numberOfFilters = numberOfFilters,
convolutionKernelSize = 3, deconvolutionKernelSize = 2 )
unetModel$load_weights( weightsFileName )
batchX <- array( data = 0, dim = c( 1, dim( template ), channelSize ) )
batchX[1,,,,1] = as.array( imageWarped )
for( i in seq.int( length( templatePriors ) ) )
{
batchX[1,,,,i+1] <- as.array( templatePriors[[i]] )
}
if( verbose )
{
message( "Prediction." )
}
predictedData <- drop( unetModel$predict( batchX, verbose = verbose ) )
referenceImage <- image
if( returnIsotropicOutput )
{
newSpacing <- rep( min( antsGetSpacing( image ) ), 3 )
referenceImage <- resampleImage( image, newSpacing, useVoxels = FALSE, interpType = 0 )
}
probabilityImages <- list()
for( i in seq.int( numberOfClassificationLabels ) )
{
if( verbose )
{
message( "Reconstructing image ", i, "\n" )
}
probabilityImage <- as.antsImage( predictedData[,,,i], reference = template )
probabilityImages[[i]] <- antsApplyTransforms( fixed = referenceImage,
moving = probabilityImage, transformlist = reg$invtransforms,
whichtoinvert = c( TRUE ), interpolator = "linear", verbose = verbose )
}
imageMatrix <- imageListToMatrix( probabilityImages, referenceImage * 0 + 1 )
segmentationMatrix <- matrix( apply( imageMatrix, 2, which.max ), nrow = 1 )
segmentationImage <- matrixToImages( segmentationMatrix, referenceImage * 0 + 1 )[[1]] - 1
results <- list( segmentationImage = segmentationImage,
probabilityImages = probabilityImages )
return( results )
} else {
stop( "Unrecognized parcellation." )
}
}
#' Mouse brain cortical thickness using deep learning
#'
#' Perform KellyKapowski cortical thickness
#'
#' @param t2 input 3-D unprocessed T2-weighted mouse brain image
#' @param mask Brain mask. If not specified, one is estimated using ANTsXNet mouse brain
#' extraction.
#' @param returnIsotropicOutput The network actually learns an interpolating
#' function specific to the mouse brain. Setting this to true, the output
#' images are returned isotropically resampled.
#' @param verbose print progress.
#' @return Cortical thickness image and segmentation probability images.
#' @author Tustison NJ
#' @examples
#' \dontrun{
#' library( ANTsRNet )
#' library( keras )
#'
#' image <- antsImageRead( "t1w_image.nii.gz" )
#' kk <- corticalThickness( image )
#' }
#' @export
mouseCorticalThickness <- function( t2, mask = NULL,
returnIsotropicOutput = FALSE,
verbose = FALSE )
{
parcellation <- mouseBrainParcellation( t2, mask = mask, whichParcellation = "nick",
returnIsotropicOutput = TRUE,
verbose = verbose )
# Kelly Kapowski cortical thickness
kkSegmentation <- antsImageClone(parcellation$segmentationImage)
kkSegmentation[kkSegmentation == 2] <- 3
kkSegmentation[kkSegmentation == 1] <- 2
kkSegmentation[kkSegmentation == 6] <- 2
corticalMatter <- parcellation$probabilityImages[[2]] + parcellation$probabilityImages[[7]]
otherMatter <- parcellation$probabilityImages[[3]] + parcellation$probabilityImages[[4]]
kk <- kellyKapowski( s = kkSegmentation, g = corticalMatter, w = otherMatter,
its = 45, r = 0.0025, m = 1.5, x = 0, t = 10, verbose = verbose )
if( ! returnIsotropicOutput )
{
kk <- resampleImage( kk, antsGetSpacing( t2 ), useVoxels = FALSE, interpType = 0 )
parcellation$segmentationImage <- resampleImage( parcellation$segmentationImage,
antsGetSpacing( t2 ), useVoxels = FALSE,
interpType = 1 )
for( i in seq.int( length( parcellation$probabilityImages ) ) )
{
parcellation$probabilityImages[[i]] <- resampleImage( parcellation$probabilityImages[[i]],
antsGetSpacing( t2 ), useVoxels = FALSE,
interpType = 0 )
}
}
return( list(
thicknessImage = kk,
parcellation = parcellation
) )
}
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