Hey Freesurfers,
I have a question regarding segmentation in autorecon2. I have a bunch of
sagittally-acquired MRI data -- Freesurfer has done a decent job after we
manually added control points on the data and resegmented. The problem is
that FS ends up leaving out some lateral areas of cortex. After control
points, I am satisfied with the grey/white boundary but the pial surface
could stand improvement. I am attaching an image to this email showing a
segmentation in the coronal plane. Seems like a chunk or lateral temporal
cortex is missing in the left hemi (and a little bit of missing cortex in
right hemi too.) I turned the brightness up a bit so you could see the
missing cortex in brainmask.mgz -- the lateral stuff as you can tell is not
quite as intense as the more medial voxels, which is probably the root of
the problem.
First of all, I want to get a sense of what is reasonable to expect. Does
this look like a reasonable segmentation or is it reasonable to try and
improve this? My PI is concerned about the missing lateral GM in any case.
Second, I was curious if any experienced users had advice regarding ways to
get this missing cortex included in segmentation - specifically the
situation in the left temporal lobe. Going off the website, since the
white/grey surface looks correct, control points should not be used. (I
don't know whether putting a few *right* outside the surface might help
expand pial surface? I might try that more if you think it could help..) The
guide would then suggest that pial surface edits must be made manually on
every slice prior to re-running autorecon2-pial. This would take a long
time for 1 person with 34 brains, but is certainly a possibility. Downside
to this is it becomes somewhat subjective for someone with only a
rudimentory understanding of neuroanatomy.
What if I could run the autorecon2 workflow step-by-step and try adjusting
normalization parameters? There are two normalizations here, using
mri_ca_normalize (-canorm) and mri_normalize (-normalization2).
mri_ca_normalize has a flag -p, that specifies percentage of likely wm to
use as control points (defaulting to 50%). Bumping this up may improve
intial segmentations since we've had to extensively add control points,
though I suppose the end result would be about the same (in theory).
mri_normalize has a number of parameters that might effect this, as well as
various programs called in the segmentation process.
Long story short, does anyone know of any parameter adjustment approach to
this, or is manual editting of pial surface the only solution?
Thanks!
John Sheppard