• We are often interested in subdividing or segmenting the brain into meaningful biological regions of interest (ROIs) for an analysis.
• Examples: tissue segmentation, segmentation of deep gray matter structures, segmentation of pathology (MS lesions, tumors, …)
• We will perform 3-class tissue segmentation in R using fslr and ANTsR:
  • Cerebrospinal fluid (CSF)
  • Gray Matter (GM)
  • White Matter (WM)

• Let's read in the training T1 and brain mask for training subject 02.

library(ms.lesion)
library(neurobase)
all_files = get_image_filenames_list_by_subject(
  group = "training", 
  type = "coregistered")
files = all_files$training02 
t1 = readnii(files["T1"])
mask = readnii(files["Brain_Mask"])

• Many tissue class segmentations are based on k-means clustering.
• These methods can be skewed by large outliers.

hist(t1, mask = mask, breaks = 2000); text(x = 600, y = 40000, '"outliers"')

ortho2(rt1, t1 > 400, col.y = alpha("red", 0.5)) # xyz - cog of a region

rt1 = robust_window(t1)
hist(rt1, mask = mask, breaks = 2000);