Brain Extraction/Segmentation

All code for this document is located at here.

In this tutorial we will discuss performing brain segmentation using the brain extraction tool (BET) in fsl and a robust version using a wrapper function in extrantsr, fslbet_robust.

Data Packages

For this analysis, I will use one subject from the Kirby 21 data set. The kirby21.base and kirby21.fmri packages are necessary for this analysis and have the data we will be working on. You need devtools to install these. Please refer to installing devtools for additional instructions or troubleshooting.

packages = installed.packages()
packages = packages[, "Package"]
if (!"kirby21.base" %in% packages) {
if (!"kirby21.t1" %in% packages) {

Loading Data

We will use the get_image_filenames_df function to extract the filenames on our hard disk for the T1 image.

fnames = get_image_filenames_df(ids = 113, 
                    modalities = c("T1"), 
                    visits = c(1),
                    long = FALSE)
t1_fname = fnames$T1[1]

T1 image

Let’s take a look at the T1-weighted image.

t1 = readnii(t1_fname)

rm(list = "t1")

Here we see the brain and other parts of the image are present. Most notably, the neck of the subject was imaged. Sometimes this can cause problems with segmentation and image registration.

Attempt 1: Brain Extraction of T1 image using BET

Here we will use FSL’s Brain Extraction Tool (BET) to extract the brain tissue from the rest of the image.

outfile = nii.stub(t1_fname, bn = TRUE)
outfile = paste0(outfile, "_SS_Naive.nii.gz")
if (!file.exists(outfile)) {
  ss_naive = fslbet(infile = t1_fname, outfile = outfile)
} else {
  ss_naive = readnii(outfile)