Goals and Overall Approach

We will use multiple packages and pieces of software for white matter (and gray matter/cerebro spinal fluid (CSF)) segmentation.

The overall approach will be, with the required packages in parentheses:

1. N4 Inhomogeneity Bias-Field Correction (extrantsr and ANTsR)
2. Brain extraction using BET and additional tools (extrantsr and fslr)
3. FAST for tissue-class segmentation. (fslr)

Installing Packages

Below is a script to install all the current development versions of all packages. The current fslr packages depends on oro.nifti (>= 0.5.0) , which is located at muschellij2/oro.nifti or bjw34032/oro.nifti.

Note, the ITKR and ANTsR packages can take a long time to compile. The extrantsr package builds on ANTsR and makes some convenience wrapper functions.

devtools::install_github("muschellij2/oro.nifti")
devtools::install_github("muschellij2/fslr")
devtools::install_github("stnava/cmaker")
devtools::install_github("stnava/ITKR")
devtools::install_github("stnava/ANTsR")
devtools::install_github("muschellij2/extrantsr")
install.packages("scales")

Load in the packages

Here we will load in the required packages. The scales package is imported just for the alpha function, used below in plotting.

rm(list=ls())
library(fslr)
library(extrantsr)
library(scales)

Specifying FSL path

For fslr to work, FSL must be installed. If run in the Terminal, the FSLDIR environmental variable should be found using R's Sys.getenv("FSLDIR") function.

If run in an IDE (such as RStudio or the R GUI), R must know the path of FSL, as set by the following code:

options(fsl.path="/usr/local/fsl/")

Image Filenames

Here we will set the image name. The nii.stub function will strip off the .nii.gz from img.name.

img.name = "SUBJ0001-01-MPRAGE.nii.gz"
img.stub = nii.stub(img.name)

N4 Bias Field Correction

The first step in most MRI analysis is performing inhomogeneity correction. The extrantsr function bias_correct can perform N3 or N4 bias correction from the ANTsR package.

n4img = bias_correct( img.name, correction = "N4", 
                      outfile = paste0(img.stub, "_N4.nii.gz") )

ortho2(n4img)

Let us note that the image is of the head and a bit of the neck. We wish to perform white matter segmentation only on the brain tissues, so we will do brain extraction.

Brain Extraction

The extrantsr function fslbet_robust performs brain extraction. It relies on the fslr function fslbet which calls bet from FSL. It also performs neck removal (remove.neck = TRUE) and will perform BET once and then estimate a new center of gravity (COG) and then re-run BET. These functions are implemented in fslbet specifically, but these have been re-implemented in fslbet_robust in a slightly different way. fslbet_robust will also perform N4 inhomogeneity correction, but as this has already been performed above, we will set correct = FALSE.

For neck removal, a template brain and mask must be specified. We will use the T1, 1mm resolution, MNI brain included with FSL's installation.

bet = fslbet_robust(img = n4img, 
                    retimg = TRUE,
                    remove.neck = TRUE,
                    robust.mask = FALSE,
                    template.file = file.path( fsldir(), 
                                               "data/standard", 
                                               "MNI152_T1_1mm_brain.nii.gz"),
                    template.mask = file.path( fsldir(), 
                                               "data/standard", 
                                               "MNI152_T1_1mm_brain_mask.nii.gz"), 
                    outfile = "SUBJ0001-01-MPRAGE_N4_BET", 
                    correct = FALSE)

The results look good – the brain tissue is kept (in red) only. Not much brain tissue is discarded nor non-brain-tissue is included.

ortho2(n4img, bet > 0, 
       col.y=alpha("red", 0.5))

FAST Image Segmentation

Now that we have a brain image, we can use FAST for image segmentation. We will use the fslr function fast, which calls fast from FSL. We will pass the -N option so that FAST will not perform inhomogeneity correction (different from N4 and N3), because we had performed this before.

fast = fast(file = bet, 
            outfile = paste0(img.stub, "_BET_FAST"), 
            opts = '-N')

White Matter Results

By default, FAST assumes 3 tissue classes, generally white matter, gray matter, and CSF. These are generally ordered by the mean intensity of the class. For T1-weighted images, white matter is the highest intensity, and assigned class 3. Let's see the results:

ortho2(bet, fast == 3, 
       col.y=alpha("red", 0.5))

Gray Matter / CSF Results

We can also visualize the classes for 1 and 2 for CSF and gray matter, respectively.

ortho2(bet, fast == 1, col.y=alpha("red", 0.5), text="CSF Results")

ortho2(bet, fast == 2, col.y=alpha("red", 0.5), text="Gray MatternResults")

The results indicate good segmentation of the T1 image. The fslr function fast result in more than the tissue-class segmentation, see the other files output:

list.files(pattern=paste0(img.stub, "_BET_FAST"))

[1] "SUBJ0001-01-MPRAGE_BET_FAST_mixeltype.nii.gz"
[2] "SUBJ0001-01-MPRAGE_BET_FAST_pve_0.nii.gz"    
[3] "SUBJ0001-01-MPRAGE_BET_FAST_pve_1.nii.gz"    
[4] "SUBJ0001-01-MPRAGE_BET_FAST_pve_2.nii.gz"    
[5] "SUBJ0001-01-MPRAGE_BET_FAST_pveseg.nii.gz"   
[6] "SUBJ0001-01-MPRAGE_BET_FAST_seg.nii.gz"      

Conclusions

It's a exciting time to be working in neuroimaging in R. The fslr and ANTsR packages provide functionality to perform operations for neuroimaging processing. I will be doing a series on some of the options for analysis in the coming weeks. The code for this analysis (and the data) is located at https://github.com/muschellij2/HopStat/blob/gh-pages/White_Matter_Segmentation_in_R/

Notes

The fslr function ortho2 is a rewrite of the oro.nifti::orthographic function, but with different defaults and will set values of 0 in the second image (y argument) to NA.