Dear Prad,
There is a “secret” way of getting the posteriors. You need to set the 
environment variable WRITE_POSTERIORS to 1 before you call recon-all.
In (t)csh:
setenv WRITE_POSTERIORS 1
In bash:
export WRITE_POSTERIORS=1
And then call recon-all -hippocampal-subfields-T1(T2) as usual.

The discrete segmentation picks, for each voxel, the most likely label. The 
posterior of this label might or might not be above 0.5. For instance, if a 
voxel has p=0.4 of being CA1, p=0.3 of being background, and p=0.3 of being 
fimbria, it will be labeled as CA1 (even though p<0.5 for such subfield). Some 
people seem to prefer a 2-stage approach, in which a hippocampal mask is first 
created by selecting the voxels for which p(background)<0.5, and then each 
voxel within that mask is colored with the most likely subfield at that 
location.

I hope this helps!

Cheers,

/Eugenio



Juan Eugenio Iglesias
ERC Senior Research Fellow
Translational Imaging Group
University College London
http://www.jeiglesias.com
http://cmictig.cs.ucl.ac.uk/


On 21 Mar 2017, at 20:09, Bharadwaj, Pradyumna - (prad) 
<[email protected]<mailto:[email protected]>> wrote:

Hello Dr. Iglesias,

I'm currently examining the distributions of the posterior probabilities of the 
different hippocampal subfields, and I'm interested in obtaining the 
probability distribution images for the subfields that correspond to the 
volumes output in the subjects text file.

While I can use mri_binarize on ?h.hippoSfLabels-T1.v10.mgz to obtain the 
subfield masks and use them to obtain the posterior probabilities distribution 
after soft segmentation,
is there some other straighforward way to obtain these posterior probabilities 
for the volumes stated in the output text file ?

A follow up question - It appears that the soft segmentation approach 
approximates thresholding the posteriors by a value around 0.5 ? In your 
opinion, is this value acceptable and have you found similar values in your 
testing ?

Thanks!
-Prad


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