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Imaging segmentation using antspynet

Source: R/segmentation.R
antspynet_segmentation.Rd

Supports Desikan-Killiany-Tourville labeling and deep 'Atropos'.

Usage

antspynet_desikan_killiany_tourville_labeling(
  x,
  do_preprocessing = TRUE,
  return_probability_images = FALSE,
  do_lobar_parcellation = FALSE,
  verbose = TRUE
)

antspynet_deep_atropos(
  x,
  do_preprocessing = TRUE,
  use_spatial_priors = TRUE,
  aseg_only = TRUE,
  verbose = TRUE
)

Arguments

x

'NIfTI' image or path to the image that is to be segmented

do_preprocessing

whether x is in native space and needs the be registered to template brain before performing segmentation; default is true since the model is trained with template brain. If you want to manually process the image, see antspynet_preprocess_brain_image

return_probability_images

whether to return probability images

do_lobar_parcellation

whether to perform lobar 'parcellation'

verbose

whether to print out the messages

use_spatial_priors

whether to use 'MNI' partial tissue priors

aseg_only

whether to just return the segmented image

Value

One or a list of 'ANTsImage' image instances. Please print out antspynet$desikan_killiany_tourville_labeling or antspynet$deep_atropos to see the details.

See also

antspynet$desikan_killiany_tourville_labeling, antspynet$deep_atropos

Examples



# Print Python documents
if(interactive() && ants_available("antspynet")) {
  antspynet <- load_antspynet()

  print(antspynet$deep_atropos)

  print(antspynet$desikan_killiany_tourville_labeling)
}