multi-res voxelwise neighborhood random forest segmentation

Represents multiscale feature images as a neighborhood and uses the features to apply a random forest segmentation model to a new image

mrvnrfs.predict(
  rflist,
  x,
  labelmasks,
  rad = NA,
  multiResSchedule = c(4, 2, 1),
  asFactors = TRUE,
  voxchunk = 60000
)

Arguments

rflist	a list of random forest models from mrvnrfs
x	a list of lists where each list contains feature images
labelmasks	a mask (or list of masks) used to define the area to predict. This is used to save time by contstrain the prediction in within the brain.
rad	vector of dimensionality d define nhood radius
multiResSchedule	an integer vector defining multi-res levels
asFactors	boolean - treat the y entries as factors
voxchunk	value of maximal voxels to predict at once. This value is used to split the prediction into smaller chunks such that memory requirements do not become too big

Value

list a 4-list with the rf model, training vector, feature matrix and the random mask