R/tissue_segment.R

Defines functions seg_list_to_hard t1_segment

Documented in t1_segment

#' MALF Tissue and Structure Segmentation
#'
#' @param t1 T1-weighted image/filename
#' @param outdir Output directory
#' @param num_templates Number of templates to use for MALF
#' @param interpolator interpolation done in \code{\link{registration}}
#' @param typeofTransform Transformation to align the templates to the T1
#' @param verbose Print diagnostic messages
#' @param ... additional options to pass to \code{\link{malf}}
#' @param malf_prob_function Function for MALF combination for probability.
#' @param malf_label_function Function for MALF combination for labeling.
#' @param force_registration Should the MALF registrations be re-run if
#' they already exist?
#'
#' @return Object of class nifti
#' @export
#' @importFrom extrantsr malf
#' @importFrom neurobase niftiarr
#' @importFrom neurobase check_outfile
#' @importFrom extrantsr reapply_malf
t1_segment = function(
  t1,
  outdir = tempdir(),
  num_templates = 35,
  interpolator = "genericLabel",
  typeofTransform = "SyNAggro",
  verbose = TRUE,
  malf_prob_function = "staple_prob",
  malf_label_function = "mode",
  force_registration = FALSE,
  ...){

  ind = seq(num_templates)
  templates = malf.templates::malf_images()
  brains = templates$brains[ind]
  tissues = templates$tissues[ind]
  labels = templates$labels[ind]

  args = list(...)
  if (!"outfiles" %in% names(args)) {
    outfiles = sapply(seq(num_templates), function(x) {
      tempfile(fileext = paste0("_", x, ".nii.gz"))
    })
  } else {
    outfiles = args$outfiles
  }

  if (!"label_outfiles" %in% names(args)) {
    label_outfiles = sapply(seq(num_templates), function(x) {
      tempfile(fileext = paste0("_", x, ".nii.gz"))
    })
  } else {
    label_outfiles = args$label_outfiles
  }


  #######################################
  # Try MALF for Tissues with MASS Templates
  #######################################
  tissue_fname = file.path(
    outdir,
    "TISSUES.nii.gz")
  tissue_list_fnames = file.path(
    outdir,
    paste0(c("CSF", "GM", "WM"), ".nii.gz")
  )
  label_fname = file.path(
    outdir,
    "STRUCTURES.nii.gz")

  fnames = c(TISSUES = tissue_fname,
             STRUCTURES = label_fname,
             CSF = tissue_list_fnames[1],
             GM = tissue_list_fnames[2],
             WM = tissue_list_fnames[3])

  if (!all_exists(fnames)) {

    args = list(...)
    inverted = args$inverted
    if (verbose > 0) {
      msg = paste0(
        "Running MALF for tissue and structures with ", num_templates,
        " templates - this may take some time")
      message(msg)
    }

    args = list(
      infile = t1,
      template.images = brains,
      template.structs = tissues,
      keep_images = TRUE,
      retimg = TRUE,
      func = malf_prob_function,
      keep_regs = TRUE,
      interpolator = interpolator,
      verbose = verbose,
      typeofTransform = typeofTransform,
      rerun_registration = force_registration,
      ...
    )
    args$outfiles = outfiles
    if (verbose) {
      message(paste("All outfiles are:",
                    paste(outfiles, collapse = ", ")))
    }

    regs = do.call(malf, args = args)

    tissue_img = seg_list_to_hard(
      regs$outimg,
      ties.method = "first") - 1
    writenii(tissue_img, filename = tissue_fname)

    tissue_list = regs$outimg[2:4]

    mapply(function(img, filename) {
      writenii(img, filename = filename)
    }, tissue_list, tissue_list_fnames)

    if (is.null(inverted)) {
      inverted = FALSE
    }

    label_img = reapply_malf(
      infile = t1,
      regs = regs$regs,
      template.structs = labels,
      keep_images = TRUE,
      retimg = FALSE,
      outfiles = label_outfiles,
      outfile = label_fname,
      func = malf_label_function,
      interpolator = interpolator,
      verbose = verbose,
      inverted = inverted
    )
    label_img = NULL
  }
  outfiles = lapply(fnames,
                    identity)
  return(outfiles)
}


seg_list_to_hard = function(
  img,
  ties.method = c("first", "last", "random")
){
  stopifnot(inherits(img, "list"))
  xmax = sapply(img, c)
  ties.method = match.arg(
    ties.method,
    c("first", "last", "random")
  )

  maxs = max.col(xmax,
                 ties.method = ties.method)
  res = neurobase::niftiarr(img[[1]], maxs)
  res
}
muschellij2/smri.process documentation built on Dec. 2, 2018, 4:22 p.m.