I see, thanks for the quick clarification. So in the Method 2 there is a chance that some voxels will show up in multiple ROIs, right? Is there a modification of Method 2 that maximizes that number of labeled voxels while ensuring that they will not show up in multiple ROIs?
Thanks again, Tim On Fri, Aug 20, 2010 at 4:15 PM, Douglas N Greve <gr...@nmr.mgh.harvard.edu>wrote: > The difference is the partial volume correction is different if there are a > bunch of other labels there (aparc+aseg) vs only one label > > doug > > Timothy Vickery wrote: > >> 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 >> ------------------------------------------------------------------------ >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> > > -- > Douglas N. Greve, Ph.D. > MGH-NMR Center > gr...@nmr.mgh.harvard.edu > Phone Number: 617-724-2358 Fax: 617-726-7422 > > Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting > FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html > >
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
