View source: R/segmentationRefinement.R
segmentationRefinement.predict | R Documentation |
A random forest implementation of the corrective learning wrapper introduced in Wang, et al., Neuroimage 2011 (http://www.ncbi.nlm.nih.gov/pubmed/21237273). The prediction process involves using the label-specific training models to refine an initial segmentation.
segmentationRefinement.predict(
segmentationImage,
labelSet,
labelModels,
featureImages,
featureImageNames,
dilationRadius = 2,
neighborhoodRadius = 0,
normalizeSamplesPerLabel = TRUE,
useEntireLabeledRegion = TRUE
)
segmentationImage |
image to refine via corrective learning. |
labelSet |
a vector specifying the labels of interest. Must be specified. |
labelModels |
a list of models. Each element of the labelSet requires a model. |
featureImages |
a list of feature images. |
featureImageNames |
is a vector of character strings naming the set of features. Must be specified. |
dilationRadius |
specifies the dilation radius for determining the ROI for each label using binary morphology. Alternatively, the user can specify a float distance value, e.g., "dilationRadius = '2.75mm'", to employ an isotropic dilation based on physical distance. For the latter, the distance value followed by the character string 'mm' (for millimeters) is necessary. |
neighborhoodRadius |
specifies which voxel neighbors should be included in prediction. The user can specify a scalar or vector but it must match with what was used for training. |
normalizeSamplesPerLabel |
if TRUE, the samples from each ROI are normalized by the mean of the voxels in that ROI. Can be a vector (one element per feature). |
useEntireLabeledRegion |
if TRUE, estimation is performed on the
full dilated ROI for each label. If FALSE, estimation is performed on the
combined inner and outer boundary region determined by the
|
a list consisting of the refined segmentation estimate (RefinedSegmentationImage) and a list of the foreground probability images (ForegroundProbabilityImages).
Tustison NJ
## Not run:
library(ANTsR)
library(ggplot2)
imageIDs <- c("r16", "r27", "r30", "r62", "r64", "r85")
# Perform simple 3-tissue segmentation. For convenience we are
# going to use atropos segmentation to define the "ground-truth"
# segmentations and the kmeans to define the segmentation we
# want to "correct". We collect feature images for each image.
# The gradient and laplacian images chosen below as feature
# images are simply selected for convenience.
segmentationLabels <- c(1, 2, 3)
featureImageNames <- c("T1", "Gradient", "Laplacian")
images <- list()
kmeansSegs <- list()
atroposSegs <- list()
featureImages <- list()
for (i in 1:length(imageIDs))
{
cat("Processing image", imageIDs[i], "\n")
images[[i]] <- antsImageRead(getANTsRData(imageIDs[i]))
mask <- getMask(images[[i]])
kmeansSegs[[i]] <- kmeansSegmentation(images[[i]],
length(segmentationLabels), mask,
mrf = 0.0
)$segmentation
atroposSegs[[i]] <- atropos(images[[i]], mask,
i = "KMeans[3]",
m = "[0.25,1x1]", c = "[5,0]"
)$segmentation
featureImageSetPerImage <- list()
featureImageSetPerImage[[1]] <- images[[i]]
featureImageSetPerImage[[2]] <- iMath(images[[i]], "Grad", 1.0)
featureImageSetPerImage[[3]] <- iMath(images[[i]], "Laplacian", 1.0)
featureImages[[i]] <- featureImageSetPerImage
}
# Perform training. We train on images "r27", "r30",
# "r62", "r64", "r85" and
# test/predict on image "r16".
cat("\nTraining\n\n")
segLearning <- segmentationRefinement.train(
featureImages = featureImages[2:6],
truthLabelImages = atroposSegs[2:6], segmentationImages = kmeansSegs[2:6],
featureImageNames = featureImageNames, labelSet = segmentationLabels,
maximumNumberOfSamplesOrProportionPerClass = 100, dilationRadius = 1,
neighborhoodRadius = c(1, 1), normalizeSamplesPerLabel = TRUE,
useEntireLabeledRegion = FALSE
)
cat("\nPrediction\n\n")
refinement <- segmentationRefinement.predict(
segmentationImage = kmeansSegs[[1]], labelSet = segmentationLabels,
segLearning$LabelModels, featureImages[[1]], featureImageNames,
dilationRadius = 1, neighborhoodRadius = c(1, 1),
normalizeSamplesPerLabel = TRUE
)
# Compare "ground truth" = atroposSegs[[1]] with
# refinement$RefinedSegmentationImage
antsImageWrite(
refinement$RefinedSegmentationImage,
"r16RefinedSegmentation.nii.gz"
)
## End(Not run)
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