Dear Luigi, the MAP solution (which maximizes the posterior probability of the segmentation given the image and the atlas) is to assign to each voxel the label with the highest posterior probability. The expected value of the volume of a structure is indeed the integral of the its posterior across the image - which can be very different from the volume stemming from counting the voxels in the discrete MAP segmentation. Cheers, /Eugenio
Juan Eugenio Iglesias Postdoctoral researcher BCBL www.jeiglesias.com www.bcbl.eu Legal disclaimer/Aviso legal/Lege-oharra: www.bcbl.eu/legal-disclaimer ----- Original Message ----- From: "Luigi Antelmi" <luigi.ante...@gmail.com> To: freesurfer@nmr.mgh.harvard.edu Cc: "Jorge Jovicich" <jorge.jovic...@unitn.it>, "Moira Marizzoni" <mmarizz...@fatebenefratelli.it> Sent: Monday, September 8, 2014 3:57:38 PM Subject: Re: [Freesurfer] hippocampal subfields: from posterior to binary masks Dear all, I just found that the equivalence Volume/integral isn't quite correct. The equivalence must be between the volume and something like an expected value of the histogram V = E[H] = ∫xH(x) dx (where x is the greyscale value). So, the point 2) described in my previous email becomes (with some intermediate passages) Find the threshold value thr such that: Does this make any sense to you? Luigi. 2014-09-08 9:49 GMT+02:00 Luigi Antelmi < luigi.ante...@gmail.com > : Dear list members, as you know, the hippocampal subfields given back by FreeSurfer (posterior_left_CA1.mgz, posterior_left_CA2-3.mgz, etc.) are posterior probability maps in the range [0-255] (i.e. [0-1] with 8 bit of quantization). Although there is a way to calculate the volume ( kvlQuantifyPosteriorProbabilityImages ) that operates by summing up all the greyscale values (an equivalent method is to integrate the histogram over the range 0-255), to my knowledge there is no freesurfer's routine able to extract a binary mask out of the posterior probability map. What procedure do you use or suggest? I think there are two possibilities here. 1) As written in the paper from Van Leeput et al. , I can "assign each voxel to the label with the highest posterior probability" . Althought I'm wondering why such a thing, documented in the paper, has not been implemented yet, I do not think this is the right way to do it because this procedure do not make any distinction between low probability voxels and high prob ones. 2) Threshold the subfields such that the volumes of the thresholded mask will equal those calculated by integrating the histogram on the domain 0-255; Formally, find t such that: where H is the histogram of the subfield. I prefer the second option because here low prob values are discarded, and because one can apply the equation to a rigid transformed subfield, thus limiting the partial volume effect of the resampling. What do you think about? Luigi. _______________________________________________ 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.
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