diff --git a/NAMESPACE b/NAMESPACE
index b65e947..74c7185 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -101,6 +101,7 @@ export(createResNetWithSpatialTransformerNetworkModel3D)
export(createResUnetModel2D)
export(createResUnetModel3D)
export(createRmnetGenerator)
+export(createShivaUnetModel3D)
export(createSimpleClassificationWithSpatialTransformerNetworkModel2D)
export(createSimpleClassificationWithSpatialTransformerNetworkModel3D)
export(createSimpleFullyConvolutionalNeuralNetworkModel3D)
@@ -196,6 +197,7 @@ export(regressionMatchImage)
export(sampleFromCategoricalDistribution)
export(sampleFromOutput)
export(shivaPvsSegmentation)
+export(shivaWmhSegmentation)
export(simulateBiasField)
export(splitMixtureParameters)
export(sysuMediaWmhSegmentation)
diff --git a/R/createCustomUnetModel.R b/R/createCustomUnetModel.R
index cea4667..150bdd1 100644
--- a/R/createCustomUnetModel.R
+++ b/R/createCustomUnetModel.R
@@ -297,6 +297,140 @@ createHippMapp3rUnetModel3D <- function( inputImageSize,
return( unetModel )
}
+#' Implementation of the "shiva" u-net architecture for PVS and WMH
+#' segmentation
+#'
+#' Publications:
+#'
+#' * PVS: https://pubmed.ncbi.nlm.nih.gov/34262443/
+#' * WMH: https://pubmed.ncbi.nlm.nih.gov/38050769/
+#'
+#' with respective GitHub repositories:
+#'
+#' * PVS: https://github.com/pboutinaud/SHIVA_PVS
+#' * WMH: https://github.com/pboutinaud/SHIVA_WMH
+#'
+#' @param numberOfModalities Specifies number of channels in the
+#' architecture.
+#' @return a u-net keras model
+#' @author Tustison NJ
+#' @examples
+#' \dontrun{
+#'
+#' model <- createShivaUnetModel3D()
+#'
+#' }
+#' @import keras
+#' @export
+createShivaUnetModel3D <- function( numberOfModalities = 1 )
+{
+ K <- tensorflow::tf$keras$backend
+
+ getPadShape <- function( targetLayer, referenceLayer )
+ {
+ padShape <- list()
+
+ delta <- K$int_shape( targetLayer )[[2]] - K$int_shape( referenceLayer )[[2]]
+ if( delta %% 2 != 0 )
+ {
+ padShape[[1]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) + 1L )
+ } else {
+ padShape[[1]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) )
+ }
+
+ delta <- K$int_shape( targetLayer )[[3]] - K$int_shape( referenceLayer )[[3]]
+ if( delta %% 2 != 0 )
+ {
+ padShape[[2]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) + 1L )
+ } else {
+ padShape[[2]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) )
+ }
+
+ delta <- K$int_shape( targetLayer )[[4]] - K$int_shape( referenceLayer )[[4]]
+ if( delta %% 2 != 0 )
+ {
+ padShape[[3]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) + 1L )
+ } else {
+ padShape[[3]] <- c( as.integer( delta / 2 ), as.integer( delta / 2 ) )
+ }
+ if( all( padShape[[1]] == c( 0, 0 ) ) && all( padShape[[2]] == c( 0, 0 ) ) && all( padShape[[3]] == c( 0, 0 ) ) )
+ {
+ return( NULL )
+ } else {
+ return( padShape )
+ }
+ }
+ inputImageSize <- c( 160, 214, 176, numberOfModalities )
+ numberOfFilters <- c( 10, 18, 32, 58, 104, 187, 337 )
+
+ inputs <- layer_input( shape = inputImageSize )
+
+ # encoding layers
+
+ encodingLayers <- list()
+
+ outputs <- inputs
+ for( i in seq.int( length( numberOfFilters ) ) )
+ {
+ outputs <- outputs %>% layer_conv_3d( numberOfFilters[i], kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+
+ outputs <- outputs %>% layer_conv_3d( numberOfFilters[i], kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+
+ encodingLayers[[i]] <- outputs
+ outputs <- outputs %>% layer_max_pooling_3d( pool_size = 2L )
+ dropoutRate <- 0.05
+ if( i > 1 )
+ {
+ dropoutRate <- 0.5
+ }
+ outputs <- outputs %>% layer_spatial_dropout_3d( rate = dropoutRate )
+ }
+
+ # decoding layers
+
+ for( i in seq.int( from = length( encodingLayers ), to = 1, by = -1 ) )
+ {
+ upsampleLayer <- outputs %>% layer_upsampling_3d( size = 2L )
+ padShape <- getPadShape( encodingLayers[[i]], upsampleLayer )
+ if( i > 1 && ! is.null( padShape ) )
+ {
+ zeroLayer <- upsampleLayer %>% layer_zero_padding_3d( padding = padShape )
+ outputs <- layer_concatenate( list( zeroLayer, encodingLayers[[i]] ), axis = -1L, trainable = TRUE )
+ } else {
+ outputs <- layer_concatenate( list( upsampleLayer, encodingLayers[[i]] ), axis = -1L, trainable = TRUE )
+ }
+
+ outputs <- outputs %>% layer_conv_3d( K$int_shape( outputs )[[5]], kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+
+ outputs <- outputs %>% layer_conv_3d( numberOfFilters[i], kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+ outputs <- outputs %>% layer_spatial_dropout_3d( rate = 0.5 )
+ }
+
+ # final
+
+ outputs <- outputs %>% layer_conv_3d( 10, kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+
+ outputs <- outputs %>% layer_conv_3d( 10, kernel_size = 3L, padding = 'same', use_bias = FALSE )
+ outputs <- outputs %>% layer_batch_normalization()
+ outputs <- outputs %>% layer_activation( "swish" )
+
+ outputs <- outputs %>% layer_conv_3d( 1, kernel_size = 1L, activation = "sigmoid", padding = 'same' )
+
+ unetModel <- keras_model( inputs = inputs, outputs = outputs )
+
+ return( unetModel )
+}
+
#' Implementation of the "HyperMapp3r" U-net architecture
#'
#' Creates a keras model implementation of the u-net architecture
@@ -480,7 +614,7 @@ createSysuMediaUnetModel2D <- function( inputImageSize, anatomy = c( "wmh", "cla
{
getCropShape <- function( targetLayer, referenceLayer )
{
- K <- keras::backend()
+ K <- tensorflow::tf$keras$backend
cropShape <- list()
@@ -592,7 +726,7 @@ createSysuMediaUnetModel3D <- function( inputImageSize,
{
getCropShape <- function( targetLayer, referenceLayer )
{
- K <- keras::backend()
+ K <- tensorflow::tf$keras$backend
cropShape <- list()
diff --git a/R/getPretrainedNetwork.R b/R/getPretrainedNetwork.R
index ca2c9c0..78b340e 100644
--- a/R/getPretrainedNetwork.R
+++ b/R/getPretrainedNetwork.R
@@ -123,6 +123,16 @@ getPretrainedNetwork <- function(
"pvs_shiva_t1_flair_2",
"pvs_shiva_t1_flair_3",
"pvs_shiva_t1_flair_4",
+ "wmh_shiva_flair_0",
+ "wmh_shiva_flair_1",
+ "wmh_shiva_flair_2",
+ "wmh_shiva_flair_3",
+ "wmh_shiva_flair_4",
+ "wmh_shiva_t1_flair_0",
+ "wmh_shiva_t1_flair_1",
+ "wmh_shiva_t1_flair_2",
+ "wmh_shiva_t1_flair_3",
+ "wmh_shiva_t1_flair_4",
"protonLungMri",
"protonLobes",
"pulmonaryArteryWeights",
@@ -145,7 +155,7 @@ getPretrainedNetwork <- function(
"wholeHeadInpaintingFLAIR",
"wholeHeadInpaintingPatchBasedT1",
"wholeHeadInpaintingPatchBasedFLAIR",
- "wholeTumorSegmentationT2Flair",
+ "wholeTumorSegmentationT2Flair",
"wholeLungMaskFromVentilation" ),
targetFileName, antsxnetCacheDirectory = NULL )
{
@@ -250,17 +260,27 @@ getPretrainedNetwork <- function(
mouseT2wBrainParcellation3DNick = "https://figshare.com/ndownloader/files/44714944",
mouseT2wBrainParcellation3DTct = "https://figshare.com/ndownloader/files/47214538",
mouseSTPTBrainParcellation3DJay = "https://figshare.com/ndownloader/files/46710592",
- pvs_shiva_t1_0 = "https://figshare.com/ndownloader/files/48363799",
- pvs_shiva_t1_1 = "https://figshare.com/ndownloader/files/48363832",
- pvs_shiva_t1_2 = "https://figshare.com/ndownloader/files/48363814",
- pvs_shiva_t1_3 = "https://figshare.com/ndownloader/files/48363790",
- pvs_shiva_t1_4 = "https://figshare.com/ndownloader/files/48363829",
- pvs_shiva_t1_5 = "https://figshare.com/ndownloader/files/48363823",
- pvs_shiva_t1_flair_0 = "https://figshare.com/ndownloader/files/48363784",
- pvs_shiva_t1_flair_1 = "https://figshare.com/ndownloader/files/48363820",
- pvs_shiva_t1_flair_2 = "https://figshare.com/ndownloader/files/48363796",
- pvs_shiva_t1_flair_3 = "https://figshare.com/ndownloader/files/48363793",
- pvs_shiva_t1_flair_4 = "https://figshare.com/ndownloader/files/48363826",
+ pvs_shiva_t1_0 = "https://figshare.com/ndownloader/files/48660169",
+ pvs_shiva_t1_1 = "https://figshare.com/ndownloader/files/48660193",
+ pvs_shiva_t1_2 = "https://figshare.com/ndownloader/files/48660199",
+ pvs_shiva_t1_3 = "https://figshare.com/ndownloader/files/48660178",
+ pvs_shiva_t1_4 = "https://figshare.com/ndownloader/files/48660172",
+ pvs_shiva_t1_5 = "https://figshare.com/ndownloader/files/48660187",
+ pvs_shiva_t1_flair_0 = "https://figshare.com/ndownloader/files/48660181",
+ pvs_shiva_t1_flair_1 = "https://figshare.com/ndownloader/files/48660175",
+ pvs_shiva_t1_flair_2 = "https://figshare.com/ndownloader/files/48660184",
+ pvs_shiva_t1_flair_3 = "https://figshare.com/ndownloader/files/48660190",
+ pvs_shiva_t1_flair_4 = "https://figshare.com/ndownloader/files/48660196",
+ wmh_shiva_flair_0 = "https://figshare.com/ndownloader/files/48660487",
+ wmh_shiva_flair_1 = "https://figshare.com/ndownloader/files/48660496",
+ wmh_shiva_flair_2 = "https://figshare.com/ndownloader/files/48660493",
+ wmh_shiva_flair_3 = "https://figshare.com/ndownloader/files/48660490",
+ wmh_shiva_flair_4 = "https://figshare.com/ndownloader/files/48660511",
+ wmh_shiva_t1_flair_0 = "https://figshare.com/ndownloader/files/48660529",
+ wmh_shiva_t1_flair_1 = "https://figshare.com/ndownloader/files/48660547",
+ wmh_shiva_t1_flair_2 = "https://figshare.com/ndownloader/files/48660499",
+ wmh_shiva_t1_flair_3 = "https://figshare.com/ndownloader/files/48660550",
+ wmh_shiva_t1_flair_4 = "https://figshare.com/ndownloader/files/48660544",
protonLungMri = "https://ndownloader.figshare.com/files/13606799",
protonLobes = "https://figshare.com/ndownloader/files/30678455",
pulmonaryAirwayWeights = "https://figshare.com/ndownloader/files/45187168",
diff --git a/R/whiteMatterHyperintensitySegmentation.R b/R/whiteMatterHyperintensitySegmentation.R
index 4b903fc..fec9b62 100644
--- a/R/whiteMatterHyperintensitySegmentation.R
+++ b/R/whiteMatterHyperintensitySegmentation.R
@@ -606,7 +606,7 @@ wmhSegmentation <- function( flair, t1, whiteMatterMask = NULL,
domainImageIsMask = TRUE )
}
-#' PVS/VRS segmentation.
+#' SHIVA PVS/VRS segmentation.
#'
#' Perform segmentation of perivascular (PVS) or Vircho-Robin spaces (VRS).
#' \url{https://pubmed.ncbi.nlm.nih.gov/34262443/}
@@ -733,13 +733,14 @@ shivaPvsSegmentation <- function( t1, flair = NULL,
for( i in seq.int( length( modelIds ) ) )
{
- modelFile <- getPretrainedNetwork( paste0( "pvs_shiva_t1_", modelIds[i] ),
- antsxnetCacheDirectory = antsxnetCacheDirectory )
+ modelWeightsFile <- getPretrainedNetwork( paste0( "pvs_shiva_t1_", modelIds[i] ),
+ antsxnetCacheDirectory = antsxnetCacheDirectory )
if( verbose )
{
- cat( "Loading", modelFile, "\n" )
+ cat( "Loading", modelWeightsFile, "\n" )
}
- model <- tensorflow::tf$keras$models$load_model( modelFile, compile = FALSE )
+ model <- createShivaUnetModel3D( numberOfModalities = 1 )
+ model$load_weights( modelWeightsFile )
if( i == 1 )
{
batchY <- model$predict( batchX, verbose = verbose )
@@ -761,13 +762,14 @@ shivaPvsSegmentation <- function( t1, flair = NULL,
for( i in seq.int( length( modelIds ) ) )
{
- modelFile <- getPretrainedNetwork( paste0( "pvs_shiva_t1_flair_", modelIds[i] ),
- antsxnetCacheDirectory = antsxnetCacheDirectory )
+ modelWeightsFile <- getPretrainedNetwork( paste0( "pvs_shiva_t1_flair_", modelIds[i] ),
+ antsxnetCacheDirectory = antsxnetCacheDirectory )
if( verbose )
{
- cat( "Loading", modelFile, "\n" )
+ cat( "Loading", modelWeightsFile, "\n" )
}
- model <- tensorflow::tf$keras$models$load_model( modelFile, compile = FALSE )
+ model <- createShivaUnetModel3D( numberOfModalities = 2 )
+ model$load_weights( modelWeightsFile )
if( i == 1 )
{
batchY <- model$predict( batchX, verbose = verbose )
@@ -783,4 +785,185 @@ shivaPvsSegmentation <- function( t1, flair = NULL,
direction = antsGetDirection( reorientTemplate ) )
pvs <- applyAntsrTransformToImage( invertAntsrTransform( xfrm ), pvs, t1 )
return( pvs )
+}
+
+#' SHIVA WMH segmentation.
+#'
+#' Perform segmentation of white matter hyperintensities.
+#' \url{https://pubmed.ncbi.nlm.nih.gov/38050769/}
+#' with the original implementation available here:
+#' https://github.com/pboutinaud/SHIVA_WMH
+#'
+#' @param flair input 3-D FLAIR brain image.
+#' @param t1 (Optional) input 3-D T1-weighted brain image (aligned to FLAIR image).
+#' @param whichModel integer or string. Several models were trained for the
+#' case of T1-only or T1/FLAIR image pairs. One can use a specific single
+#' trained model or the average of the entire ensemble. I.e., options are:
+#' * For T1-only: 0, 1, 2, 3, 4, 5.
+#' * For T1/FLAIR: 0, 1, 2, 3, 4.
+#' * Or "all" for using the entire ensemble.
+#' @param doPreprocessing perform n4 bias correction, intensity truncation, brain
+#' extraction.
+#' @param antsxnetCacheDirectory destination directory for storing the downloaded
+#' template and model weights. Since these can be resused, if
+#' \code{is.null(antsxnetCacheDirectory)}, these data will be downloaded to the
+#' inst/extdata/ subfolder of the ANTsRNet package.
+#' @param verbose print progress.
+#' @return probabilistic image.
+#' @author Tustison NJ
+#' @examples
+#' \dontrun{
+#' library( ANTsRNet )
+#' library( keras )
+#'
+#' t1 <- antsImageRead( "t1.nii.gz" )
+#' flair <- antsImageRead( "flair.nii.gz" )
+#' results <- wmhSegmentation( t1, flair )
+#' }
+#' @export
+shivaWmhSegmentation <- function( flair, t1 = NULL,
+ whichModel = "all", doPreprocessing = TRUE,
+ antsxnetCacheDirectory = NULL, verbose = FALSE )
+{
+ ################################
+ #
+ # Preprocess images
+ #
+ ################################
+
+ t1Preprocessed <- NULL
+ flairPreprocessed <- NULL
+ brainMask <- NULL
+
+ if( doPreprocessing )
+ {
+ if( verbose )
+ {
+ message( "Preprocess image(s).\n" )
+ }
+ flairPreprocessing <- preprocessBrainImage( flair,
+ truncateIntensity = c( 0.0, 0.99 ),
+ brainExtractionModality = "flair",
+ doBiasCorrection = TRUE,
+ doDenoising = FALSE,
+ intensityNormalizationType = "01",
+ antsxnetCacheDirectory = antsxnetCacheDirectory,
+ verbose = verbose )
+ brainMask <- thresholdImage( flairPreprocessing$brainMask, 0.5, 1, 1, 0 )
+ flairPreprocessed <- flairPreprocessing$preprocessedImage * brainMask
+
+ if( ! is.null( t1 ) )
+ {
+ t1Preprocessing <- preprocessBrainImage( t1,
+ truncateIntensity = c( 0.0, 0.99 ),
+ brainExtractionModality = NULL,
+ doBiasCorrection = TRUE,
+ doDenoising = FALSE,
+ intensityNormalizationType = "01",
+ antsxnetCacheDirectory = antsxnetCacheDirectory,
+ verbose = verbose )
+ t1Preprocessed <- t1Preprocessing$preprocessedImage * brainMask
+ }
+ } else {
+ flairPreprocessed <- antsImageClone( flair )
+ if( ! is.null( t1 ) )
+ {
+ t1Preprocessed <- antsImageClone( t1 )
+ }
+ brainMask <- thresholdImage( flair, 0, 0, 0, 1 )
+ }
+
+ imageShape <- c( 160, 214, 176 )
+ onesArray <- array( data = 1, dim = imageShape )
+ reorientTemplate <- as.antsImage( onesArray, origin = c( 0, 0, 0 ),
+ spacing = c( 1, 1, 1 ),
+ direction = diag( 3 ) )
+
+ centerOfMassTemplate <- getCenterOfMass( reorientTemplate )
+ centerOfMassImage <- getCenterOfMass( brainMask )
+ xfrm <- createAntsrTransform( type = "Euler3DTransform",
+ center = round( centerOfMassTemplate ),
+ translation = round( centerOfMassImage - centerOfMassTemplate ) )
+
+ flairPreprocessed <- applyAntsrTransformToImage( xfrm, flairPreprocessed,
+ reorientTemplate )
+ if( ! is.null( t1 ) )
+ {
+ t1Preprocessed <- applyAntsrTransformToImage( xfrm, t1Preprocessed,
+ reorientTemplate )
+ }
+
+ ################################
+ #
+ # Load models and predict
+ #
+ ################################
+
+ batchY <- NULL
+
+ if( is.null( t1 ) )
+ {
+ batchX <- array( data = 0, dim = c( 1, imageShape, 1 ) )
+ batchX[1,,,,1] <- as.array( flairPreprocessed )
+
+ modelIds <- c( whichModel )
+ if( whichModel == "all" )
+ {
+ modelIds <- c( 0, 1, 2, 3, 4 )
+ }
+
+ for( i in seq.int( length( modelIds ) ) )
+ {
+ modelWeightsFile <- getPretrainedNetwork( paste0( "wmh_shiva_flair_", modelIds[i] ),
+ antsxnetCacheDirectory = antsxnetCacheDirectory )
+ if( verbose )
+ {
+ cat( "Loading", modelWeightsFile, "\n" )
+ }
+ model <- createShivaUnetModel3D( numberOfModalities = 1 )
+ model$load_weights( modelWeightsFile )
+ if( i == 1 )
+ {
+ batchY <- model$predict( batchX, verbose = verbose )
+ } else {
+ batchY <- batchY + model$predict( batchX, verbose = verbose )
+ }
+ }
+ batchY <- batchY / length( modelIds )
+ } else {
+ batchX <- array( data = 0, dim = c( 1, imageShape, 2 ) )
+ batchX[1,,,,1] <- as.array( t1Preprocessed )
+ batchX[1,,,,2] <- as.array( flairPreprocessed )
+
+ modelIds <- c( whichModel )
+ if( whichModel == "all" )
+ {
+ modelIds <- c( 0, 1, 2, 3, 4 )
+ }
+
+ for( i in seq.int( length( modelIds ) ) )
+ {
+ modelWeightsFile <- getPretrainedNetwork( paste0( "wmh_shiva_t1_flair_", modelIds[i] ),
+ antsxnetCacheDirectory = antsxnetCacheDirectory )
+ if( verbose )
+ {
+ cat( "Loading", modelWeightsFile, "\n" )
+ }
+ model <- createShivaUnetModel3D( numberOfModalities = 2 )
+ model$load_weights( modelWeightsFile )
+ if( i == 1 )
+ {
+ batchY <- model$predict( batchX, verbose = verbose )
+ } else {
+ batchY <- batchY + model$predict( batchX, verbose = verbose )
+ }
+ }
+ batchY <- batchY / length( modelIds )
+ }
+
+ wmh <- as.antsImage( drop( batchY ), origin = antsGetOrigin( reorientTemplate ),
+ spacing = antsGetSpacing( reorientTemplate ),
+ direction = antsGetDirection( reorientTemplate ) )
+ wmh <- applyAntsrTransformToImage( invertAntsrTransform( xfrm ), wmh, flair )
+ return( wmh )
}
\ No newline at end of file
diff --git a/man/createShivaUnetModel3D.Rd b/man/createShivaUnetModel3D.Rd
new file mode 100644
index 0000000..b8b2a4d
--- /dev/null
+++ b/man/createShivaUnetModel3D.Rd
@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/createCustomUnetModel.R
+\name{createShivaUnetModel3D}
+\alias{createShivaUnetModel3D}
+\title{Implementation of the "shiva" u-net architecture for PVS and WMH
+segmentation}
+\usage{
+createShivaUnetModel3D(numberOfModalities = 1)
+}
+\arguments{
+\item{numberOfModalities}{Specifies number of channels in the
+architecture.}
+}
+\value{
+a u-net keras model
+}
+\description{
+Publications:
+
+\if{html}{\out{}}\preformatted{* PVS: https://pubmed.ncbi.nlm.nih.gov/34262443/
+* WMH: https://pubmed.ncbi.nlm.nih.gov/38050769/
+}\if{html}{\out{
}}
+}
+\details{
+with respective GitHub repositories:
+
+\if{html}{\out{}}\preformatted{* PVS: https://github.com/pboutinaud/SHIVA_PVS
+* WMH: https://github.com/pboutinaud/SHIVA_WMH
+}\if{html}{\out{
}}
+}
+\examples{
+\dontrun{
+
+model <- createShivaUnetModel3D()
+
+}
+}
+\author{
+Tustison NJ
+}
diff --git a/man/getPretrainedNetwork.Rd b/man/getPretrainedNetwork.Rd
index c6dcca3..fc6a457 100644
--- a/man/getPretrainedNetwork.Rd
+++ b/man/getPretrainedNetwork.Rd
@@ -39,21 +39,23 @@ getPretrainedNetwork(
"mraVesselWeights_160", "mouseMriBrainExtraction",
"mouseT2wBrainExtraction3D", "mouseT2wBrainParcellation3DNick",
"mouseT2wBrainParcellation3DTct", "mouseSTPTBrainParcellation3DJay",
- "pvs_shiva_t1_0.h5", "pvs_shiva_t1_1.h5", "pvs_shiva_t1_2.h5", "pvs_shiva_t1_3.h5",
- "pvs_shiva_t1_4.h5", "pvs_shiva_t1_5.h5", "pvs_shiva_t1_flair_0.h5",
- "pvs_shiva_t1_flair_1.h5", "pvs_shiva_t1_flair_2.h5", "pvs_shiva_t1_flair_3.h5",
- "pvs_shiva_t1_flair_4.h5", "protonLungMri", "protonLobes", "pulmonaryArteryWeights",
-
- "pulmonaryAirwayWeights", "sixTissueOctantBrainSegmentation",
- "sixTissueOctantBrainSegmentationWithPriors1",
+ "pvs_shiva_t1_0", "pvs_shiva_t1_1", "pvs_shiva_t1_2", "pvs_shiva_t1_3",
+ "pvs_shiva_t1_4", "pvs_shiva_t1_5", "pvs_shiva_t1_flair_0", "pvs_shiva_t1_flair_1",
+ "pvs_shiva_t1_flair_2", "pvs_shiva_t1_flair_3", "pvs_shiva_t1_flair_4",
+ "wmh_shiva_flair_0", "wmh_shiva_flair_1", "wmh_shiva_flair_2", "wmh_shiva_flair_3",
+ "wmh_shiva_flair_4",
+ "wmh_shiva_t1_flair_0", "wmh_shiva_t1_flair_1",
+ "wmh_shiva_t1_flair_2", "wmh_shiva_t1_flair_3", "wmh_shiva_t1_flair_4",
+ "protonLungMri", "protonLobes", "pulmonaryArteryWeights", "pulmonaryAirwayWeights",
+ "sixTissueOctantBrainSegmentation", "sixTissueOctantBrainSegmentationWithPriors1",
"sixTissueOctantBrainSegmentationWithPriors2", "sysuMediaWmhFlairOnlyModel0",
"sysuMediaWmhFlairOnlyModel1", "sysuMediaWmhFlairOnlyModel2",
"sysuMediaWmhFlairT1Model0", "sysuMediaWmhFlairT1Model1",
- "sysuMediaWmhFlairT1Model2", "tidsQualityAssessment", "xrayLungOrientation",
+ "sysuMediaWmhFlairT1Model2",
+ "tidsQualityAssessment", "xrayLungOrientation",
"xrayLungExtraction", "chexnetClassificationModel",
"chexnetClassificationANTsXNetModel", "tb_antsxnet_model",
- "wholeHeadInpaintingFLAIR",
- "wholeHeadInpaintingPatchBasedT1",
+ "wholeHeadInpaintingFLAIR", "wholeHeadInpaintingPatchBasedT1",
"wholeHeadInpaintingPatchBasedFLAIR", "wholeTumorSegmentationT2Flair",
"wholeLungMaskFromVentilation"),
targetFileName,
diff --git a/man/shivaPvsSegmentation.Rd b/man/shivaPvsSegmentation.Rd
index 016eb0d..1573cd2 100644
--- a/man/shivaPvsSegmentation.Rd
+++ b/man/shivaPvsSegmentation.Rd
@@ -2,12 +2,13 @@
% Please edit documentation in R/whiteMatterHyperintensitySegmentation.R
\name{shivaPvsSegmentation}
\alias{shivaPvsSegmentation}
-\title{PVS/VRS segmentation.}
+\title{SHIVA PVS/VRS segmentation.}
\usage{
shivaPvsSegmentation(
t1,
flair = NULL,
whichModel = "all",
+ doPreprocessing = TRUE,
antsxnetCacheDirectory = NULL,
verbose = FALSE
)
@@ -24,15 +25,15 @@ trained model or the average of the entire ensemble. I.e., options are:
* For T1/FLAIR: 0, 1, 2, 3, 4.
* Or "all" for using the entire ensemble.}
+\item{doPreprocessing}{perform n4 bias correction, intensity truncation, brain
+extraction.}
+
\item{antsxnetCacheDirectory}{destination directory for storing the downloaded
template and model weights. Since these can be resused, if
\code{is.null(antsxnetCacheDirectory)}, these data will be downloaded to the
inst/extdata/ subfolder of the ANTsRNet package.}
\item{verbose}{print progress.}
-
-\item{doPreprocessing}{perform n4 bias correction, intensity truncation, brain
-extraction.}
}
\value{
probabilistic image.
diff --git a/man/shivaWmhSegmentation.Rd b/man/shivaWmhSegmentation.Rd
new file mode 100644
index 0000000..9be3c0f
--- /dev/null
+++ b/man/shivaWmhSegmentation.Rd
@@ -0,0 +1,59 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/whiteMatterHyperintensitySegmentation.R
+\name{shivaWmhSegmentation}
+\alias{shivaWmhSegmentation}
+\title{SHIVA WMH segmentation.}
+\usage{
+shivaWmhSegmentation(
+ flair,
+ t1 = NULL,
+ whichModel = "all",
+ doPreprocessing = TRUE,
+ antsxnetCacheDirectory = NULL,
+ verbose = FALSE
+)
+}
+\arguments{
+\item{flair}{input 3-D FLAIR brain image.}
+
+\item{t1}{(Optional) input 3-D T1-weighted brain image (aligned to FLAIR image).}
+
+\item{whichModel}{integer or string. Several models were trained for the
+case of T1-only or T1/FLAIR image pairs. One can use a specific single
+trained model or the average of the entire ensemble. I.e., options are:
+* For T1-only: 0, 1, 2, 3, 4, 5.
+* For T1/FLAIR: 0, 1, 2, 3, 4.
+* Or "all" for using the entire ensemble.}
+
+\item{doPreprocessing}{perform n4 bias correction, intensity truncation, brain
+extraction.}
+
+\item{antsxnetCacheDirectory}{destination directory for storing the downloaded
+template and model weights. Since these can be resused, if
+\code{is.null(antsxnetCacheDirectory)}, these data will be downloaded to the
+inst/extdata/ subfolder of the ANTsRNet package.}
+
+\item{verbose}{print progress.}
+}
+\value{
+probabilistic image.
+}
+\description{
+Perform segmentation of white matter hyperintensities.
+\url{https://pubmed.ncbi.nlm.nih.gov/38050769/}
+with the original implementation available here:
+https://github.com/pboutinaud/SHIVA_WMH
+}
+\examples{
+\dontrun{
+library( ANTsRNet )
+library( keras )
+
+t1 <- antsImageRead( "t1.nii.gz" )
+flair <- antsImageRead( "flair.nii.gz" )
+results <- wmhSegmentation( t1, flair )
+}
+}
+\author{
+Tustison NJ
+}