Description Usage Arguments See Also Examples
Uses ortho2
to plot differences between a predicted binary
image and the assumed ground truth (roi
).
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img |
image to be underlaid |
pred |
binary segmentation (prediction) |
roi |
binary manual segmentation (ground truth) |
xyz |
coordinate for the center of the crosshairs. |
cols |
colors for false negatives/positives |
levels |
labels for false negatives/positives |
addlegend |
add legend, passed to |
center |
run |
leg.cex |
multiplier for legend size |
... |
arguments to be passed to |
x |
List of images of class |
z |
slice to display |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | set.seed(5)
dims = rep(10, 3)
arr = array(rpois(prod(dims), lambda = 2), dim = dims)
nim = oro.nifti::nifti(arr)
roi = nim > 2
pred = nim > 1.5
ortho_diff(nim, pred, roi)
set.seed(5)
dims = rep(10, 3)
arr = array(rnorm(prod(dims)), dim = dims)
nim = oro.nifti::nifti(arr)
mask = nim > 2
pred = nim > 1.5
multi_overlay_diff(nim, roi = mask, pred = pred)
if (requireNamespace("brainR", quietly = TRUE)) {
visits = 1:3
y = paste0("Visit_", visits, ".nii.gz")
y = system.file(y, package = "brainR")
y = lapply(y, readnii)
y = lapply(y, function(r){
pixdim(r) = c(0, rep(1, 3), rep(0, 4))
dropImageDimension(r)
})
x = system.file("MNI152_T1_1mm_brain.nii.gz",
package = "brainR")
x = readnii(x)
mask = x > 0
alpha = function(col, alpha = 1) {
cols = t(col2rgb(col, alpha = FALSE)/255)
rgb(cols, alpha = alpha)
}
roi = y[[2]]
pred = y
multi_overlay_diff(x, roi = roi, pred = pred)
multi_overlay_diff(x, roi = roi, pred = pred,
mask = mask,
main = paste0("\n", "Visit ", visits),
text = LETTERS[visits],
text.x = 0.9,
text.y = 0.1,
text.cex = 3)
}
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