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Hi Krzysztof,

Samseg will "eat" anything you throw at it. That doesn't mean that the type
of input data doesn't matter: the higher the contrast-to-noise ratio for
the specific structure(s) you're interested in, the better the results will
be. Note that you can often increase the contrast-to-noise ratio by giving
several contrast combinations (i.e., multi-contrast input data) to samseg,
although image quality issues (e.g., low resolution, imaging artifacts,
...) can have a negative overall impact. You may want to experiment with
various combinations of your data to pick the best one suited for your
problem...

Regarding the output, specifically for lesions the main output is the
seg.mgz file, which contains a volumetric labeled image where every voxel
has been assigned to a neuroanatomical structure (the level of lesions is
99). You can also save posterior probability maps for the various
structures (incl. lesions), as explained on the wiki. This maybe be useful
if you're interested in trading off false positive vs. false negative
lesion detections differently from the default setting. FreeSurfer tools
such as mri_binarize and mri_segstats can be used to obtain lesion volumes
for different thresholds.

Hope this helps,

Koen



On Thu, Feb 29, 2024 at 6:47 PM Krzysztof Barć <kb...@ipin.edu.pl> wrote:

>         External Email - Use Caution
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> Good morning,
>
> 1) For the Samseg protocol, are only T1 and FLAIR sequences required (no
> MPRAGE scans required)?
> 2) What does the numerical output of the Samseg calculation look like?
>
> Best regards,
> Krzysztof Barć
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