Hi all,
I'm creating binary mask volumes in a subject's native functional space from the segmented brain in aparc+aseg.mgz (FS v 4.5). I have found that doing this two different ways produces different results, and I'm wondering if anyone can illuminate why this might occur and which method is more appropriate (or what other method you would suggest)...
Method 1: Resample aparc+aseg.mgz into native functional space using mri_label2vol --seg aparc_aseg.mgz --fillthresh 0.5 [...plus the rest of the appropriate inputs such as subject's bold/register.dat]
Then I just parse the resulting image (using matlab or python code) into separate binary masks for each unique identifier that I'm using...E.g., for right IPL I load this image and create a new volume [newVol = (oldVol==2008)] and save that out.
Method 2: Create a label file from aparc+aseg.mgz for each unique identifier that I'm using, and then use mri_label2vol to produce a binary mask in native functional space: mri_cor2label --i aparc+aseg.mgz --id 2008 --l rIPL.label mri_label2vol --label rIPL.label --fillthresh 0.5 [... plus the rest of the required inputs, same as those used in Method 1]
Even though these seem like they should be equivalent to me, and although the masks produced agree for the most part, I generally get several more voxels per ROI using Method 2 than I do using Method 1 (and not complete overlap otherwise). For instance, for one subject, Method 1 yields 308 voxels in rIPL, but Method 2 yields 316 voxels; disagreement between the two occurs in a total of 26 of those voxels, so it isn't just a matter of Method 2 being more generous. The discrepancy seems to be proportional to the size of the ROI, so I get just a handful of disagreements for smaller ROIs (but it seems to happen almost all the time).
Thanks for any advice on which method is better, or a suggestion of a better method.
Best, Tim