ENH: Add HOA subcortical labeling.

NAMESPACE

@@ -136,6 +136,7 @@ export(getMixtureDensityLossFunction)
 export(getMixtureDensityMseAccuracyFunction)
 export(getMixtureDensitySamplingFunction)
 export(getPretrainedNetwork)
+export(harvardOxfordAtlasLabeling)
 export(hippMapp3rSegmentation)
 export(histogramWarpImageIntensities)
 export(hyperMapp3rSegmentation)

R/brainExtraction.R

@@ -15,7 +15,7 @@
 #'   \item{"t1combined": }{Brian's combination of "t1" and "t1nobrainer".  One can also specify
 #'                         "t1combined[X]" where X is the morphological radius.  X = 12 by default.}
 #'   \item{"t1threetissue": }{T1-weighted MRI---originally developed from BrainWeb20 (and later expanded). 
-#'                        Label 1:  parenchyma, label 2: meninges/csf, label 3: misc. head.
+#'                        Label 1:  parenchyma, label 2: meninges/csf, label 3: misc. head.}
 #'   \item{"flair": }{FLAIR MRI.}
 #'   \item{"t2": }{T2-w MRI.}
 #'   \item{"bold": }{3-D mean BOLD MRI.}

R/desikanKillianyTourvilleLabeling.R

@@ -234,7 +234,7 @@ desikanKillianyTourvilleLabeling <- function( t1, doPreprocessing = TRUE,
   #
   ################################
 
-  t1Preprocessed <- t1
+  t1Preprocessed <- antsImageClone( t1 )
   if( doPreprocessing )
     {
     t1Preprocessing <- preprocessBrainImage( t1,

R/getPretrainedNetwork.R

@@ -82,6 +82,7 @@ getPretrainedNetwork <- function(
               "dktInner",
               "dktOuter",
               "dktOuterWithSpatialPriors",
+              "HarvardOxfordAtlasSubcortical",
               "ex5_coronal_weights",
               "ex5_sagittal_weights",
               "allen_brain_mask_weights",
@@ -154,7 +155,7 @@ getPretrainedNetwork <- function(
               "wholeHeadInpaintingFLAIR",
               "wholeHeadInpaintingPatchBasedT1",
               "wholeHeadInpaintingPatchBasedFLAIR",
-	             "wholeTumorSegmentationT2Flair",
+	            "wholeTumorSegmentationT2Flair",
               "wholeLungMaskFromVentilation",
               "DeepAtroposHcpT1Weights",
               "DeepAtroposHcpT1T2Weights",
@@ -237,6 +238,7 @@ getPretrainedNetwork <- function(
     dktInner = "https://ndownloader.figshare.com/files/23266943",
     dktOuter = "https://ndownloader.figshare.com/files/23765132",
     dktOuterWithSpatialPriors = "https://ndownloader.figshare.com/files/24230768",
+    HarvardOxfordAtlasSubcortical = "https://figshare.com/ndownloader/files/50577546",
     e13x5_coronal_weights = "https://figshare.com/ndownloader/files/38035968",
     e13x5_sagittal_weights = "https://figshare.com/ndownloader/files/38035965",
     allen_brain_mask_weights = "https://figshare.com/ndownloader/files/36999880",

R/harvardOxfordAtlasLabeling.R

@@ -0,0 +1,223 @@
+#' Subcortical and cerebellar labeling from a T1 image.
+#'
+#' Perform HOA labeling using deep learning and data from 
+# "High Resolution, Comprehensive Atlases of the Human Brain 
+#' Morphology" number: "NIH NIMH 5R01MH112748-04". Repository: 
+#' https://github.com/HOA-2/SubcorticalParcellations'
+#'
+#' The labeling is as follows:
+#' \itemize{
+#' \item{Label 1:}{Lateral Ventricle Left}
+#' \item{Label 2:}{Lateral Ventricle Right}
+#' \item{Label 3:}{CSF}
+#' \item{Label 4:}{Third Ventricle}
+#' \item{Label 5:}{Fourth Ventricle}
+#' \item{Label 6:}{5th Ventricle}
+#' \item{Label 7:}{Nucleus Accumbens Left}
+#' \item{Label 8:}{Nucleus Accumbens Right}
+#' \item{Label 9:}{Caudate Left}
+#' \item{Label 10:}{Caudate Right}
+#' \item{Label 11:}{Putamen Left}
+#' \item{Label 12:}{Putamen Right}
+#' \item{Label 13:}{Globus Pallidus Left}
+#' \item{Label 14:}{Globus Pallidus Right}
+#' \item{Label 15:}{Brainstem}
+#' \item{Label 16:}{Thalamus Left}
+#' \item{Label 17:}{Thalamus Right}
+#' \item{Label 18:}{Inferior Horn of the Lateral Ventricle Left}
+#' \item{Label 19:}{Inferior Horn of the Lateral Ventricle Right}
+#' \item{Label 20:}{Hippocampal Formation Left}
+#' \item{Label 21:}{Hippocampal Formation Right}
+#' \item{Label 22:}{Amygdala Left}
+#' \item{Label 23:}{Amygdala Right}
+#' \item{Label 24:}{Optic Chiasm}
+#' \item{Label 25:}{VDC Anterior Left}
+#' \item{Label 26:}{VDC Anterior Right}
+#' \item{Label 27:}{VDC Posterior Left}
+#' \item{Label 28:}{VDC Posterior Right}
+#' \item{Label 29:}{Cerebellar Cortex Left}
+#' \item{Label 30:}{Cerebellar Cortex Right}
+#' \item{Label 31:}{Cerebellar White Matter Left}
+#' \item{Label 32:}{Cerebellar White Matter Right}
+#' }
+#'
+#' Preprocessing on the training data consisted of:
+#'    * n4 bias correction,
+#'    * brain extraction, and
+#'    * affine registration to HCP.
+#' The input T1 should undergo the same steps.  If the input T1 is the raw
+#' T1, these steps can be performed by the internal preprocessing, i.e. set
+#' \code{doPreprocessing = TRUE}
+#'
+#' @param t1 raw or preprocessed 3-D T1-weighted brain image.
+#' @param doPreprocessing perform preprocessing.  See description above.
+#' @param verbose print progress.
+#' @return list consisting of the segmentation image and probability images for
+#' each label.
+#' @author Tustison NJ
+#' @examples
+#' \dontrun{
+#' library( ANTsRNet )
+#' library( keras )
+#'
+#' image <- antsImageRead( "t1.nii.gz" )
+#' results <- harvardOxfordAtlasLabeling( image )
+#' }
+#' @export
+harvardOxfordAtlasLabeling <- function( t1, doPreprocessing = TRUE,
+  verbose = FALSE )
+{
+
+  if( t1@dimension != 3 )
+    {
+    stop( "Image dimension must be 3." )
+    }
+
+  reshapeImage <- function( image, cropSize, interpType = "linear" )
+    {
+    imageResampled <- NULL
+    if( interpType == "linear" )
+      {
+      imageResampled <- resampleImage( image, c( 1, 1, 1 ), useVoxels = FALSE, interpType = 0 )
+      } else {
+      imageResampled <- resampleImage( image, c( 1, 1, 1 ), useVoxels = FALSE, interpType = 1 )
+      }
+    imageCropped <- padOrCropImageToSize( imageResampled, cropSize ) 
+    return( imageCropped )
+    }
+
+  whichTemplate <- "hcpyaT1Template"
+  templateTransformType <- "antsRegistrationSyNQuick[a]"
+  template <- antsImageRead( getANTsXNetData( whichTemplate ) )
+
+  croppedTemplateSize <- c( 160, 176, 160 )
+
+  ################################
+  #
+  # Preprocess image
+  #
+  ################################
+
+  t1Preprocessed <- antsImageClone( t1 )
+  if( doPreprocessing )
+    {
+    t1Preprocessing <- preprocessBrainImage( t1,
+        truncateIntensity = NULL,
+        brainExtractionModality = "t1threetissue",
+        template = whichTemplate,
+        templateTransformType = templateTransformType,
+        doBiasCorrection = TRUE,
+        doDenoising = FALSE,
+        verbose = verbose )
+    t1Preprocessed <- t1Preprocessing$preprocessedImage * t1Preprocessing$brainMask
+    t1Preprocessed <- reshapeImage( t1Preprocessed, cropSize = croppedTemplateSize )
+    }
+
+  ################################
+  #
+  # Build model and load weights
+  #
+  ################################
+
+  labels <- 0:35
+  channelSize <- 1
+  numberOfClassificationLabels <- length( labels )
+
+  unetModelPre <- createUnetModel3D( c( croppedTemplateSize, channelSize ),
+    numberOfOutputs = numberOfClassificationLabels, mode = 'classification',
+    numberOfFilters = c( 16, 32, 64, 128 ), dropoutRate = 0.0,
+    convolutionKernelSize = c( 3, 3, 3 ), deconvolutionKernelSize = c( 2, 2, 2 ),
+    weightDecay = 0.0 )
+
+  penultimateLayer <- unetModelPre$layers[[length( unetModelPre$layers ) - 1]]$output
+
+  output2 <-  penultimateLayer %>%
+             keras::layer_conv_3d( filters = 1, 
+                                   kernel_size = c( 1, 1, 1 ),
+                                   activation = 'sigmoid',
+                                   kernel_regularizer = keras::regularizer_l2( 0.0 ) )
+
+  unetModel <- keras::keras_model( inputs = unetModelPre$input, 
+                                   outputs = list( unetModelPre$output, output2 ) )
+  weightsFileNamePath <- getPretrainedNetwork( "HarvardOxfordAtlasSubcortical" )
+  keras::load_model_weights_hdf5( unetModel, filepath = weightsFileNamePath )
+
+  ################################
+  #
+  # Do prediction and normalize to native space
+  #
+  ################################
+
+  if( verbose )
+    {
+    cat( "Model prediction using both the original and contralaterally flipped version\n" )
+    }
+
+  batchX <- array( data = 0, dim = c( 2, croppedTemplateSize, channelSize ) )
+  batchX[1,,,,1] <- as.array( iMath( t1Preprocessed, "Normalize" ) )
+  batchX[2,,,,1] <- batchX[1,croppedTemplateSize[1]:1,,,1] 
+
+  predictedData <- unetModel %>% predict( batchX, verbose = verbose )
+
+  probabilityImages <- list()
+
+  hoaLateralLabels <- c( 0, 3, 4, 5, 6, 15, 24 )
+  hoaLateralLeftLabels <- c( 1, 7, 9, 11, 13, 16, 18, 20, 22, 25, 27, 29, 31 )
+  hoaLateralRightLabels <- c( 2, 8, 10, 12, 14, 17, 19, 21, 23, 26, 28, 30, 32 )
+
+  hoaLabels <- list()
+  hoaLabels[[1]] <- hoaLateralLabels
+  hoaLabels[[2]] <- hoaLateralLeftLabels
+  hoaLabels[[3]] <- hoaLateralRightLabels
+
+  for( b in seq.int( 2 ) )
+    {
+    for( i in seq.int( length( hoaLabels ) ) ) 
+      {
+      for( j in seq.int( length( hoaLabels[[i]] ) ) )
+        {
+        label <- hoaLabels[[i]][j]   
+        probabilityArray <- drop( predictedData[[1]][b,,,,label+1] )
+        if( label == 0 )
+          {
+          probabilityArray <- probabilityArray + drop( rowSums( predictedData[[1]][b,,,,34:36, drop=FALSE] , dims = 4 ) )
+          }
+        if( b == 2 )
+          {
+          probabilityArray <- probabilityArray[dim( probabilityArray )[1]:1,,]
+          if( i == 2 )
+            {
+            label <- hoaLateralRightLabels[j]
+            } else if( i == 3 ) {
+            label <- hoaLateralLeftLabels[j]
+            }
+          }
+        probabilityImage <- as.antsImage( probabilityArray, reference = t1Preprocessed )
+        if( doPreprocessing )
+          {
+          probabilityImage <- padOrCropImageToSize( probabilityImage, dim( template ) )
+          probabilityImage <- antsApplyTransforms( fixed = t1, moving = probabilityImage,
+                  transformlist = t1Preprocessing$templateTransforms$invtransforms,
+                    whichtoinvert = c( TRUE ), interpolator = "linear", verbose = verbose )
+          }
+        if( b == 1 )  
+          {
+          probabilityImages[[label + 1]] <- probabilityImage
+          } else {
+          probabilityImages[[label + 1]] <- 0.5 * ( probabilityImages[[label + 1]] + probabilityImage )
+          }
+        }
+      }
+    }
+
+  imageMatrix <- imageListToMatrix( probabilityImages, t1 * 0 + 1 )
+  segmentationMatrix <- matrix( apply( imageMatrix, 2, which.max ), nrow = 1 )
+  segmentationImage <- matrixToImages( segmentationMatrix, t1 * 0 + 1 )[[1]] - 1
+
+  return( list(
+          segmentationImage = segmentationImage,
+          probabilityImages = probabilityImages
+          )
+        )
+}
+