you can play around with the fill threshold

doug

Timothy Vickery wrote:
> 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 <mailto: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
>         
> ------------------------------------------------------------------------
>
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>
>
>     -- 
>     Douglas N. Greve, Ph.D.
>     MGH-NMR Center
>     gr...@nmr.mgh.harvard.edu <mailto:gr...@nmr.mgh.harvard.edu>
>     Phone Number: 617-724-2358 Fax: 617-726-7422
>
>     Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
>     <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>
>     FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html
>     <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>
>
>

-- 
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

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