On Wed, 25 Apr 2007 08:45:17 +0200, Marko Wilke <[log in to unmask]>
>> I’m not convinced that this outcome is primarily due to the mismatch
>> between the characteristics of your study population and the template
>> (although it might be aggravated by it):
>I fully agree. Your imanges look like they were not normalized correctly
>(remember that even for native-space segmentation, the priors are in
>efect inversely normalized to the input images). Your results look like
>they are mainly the result of the prior probability maps. This can
>happen when matching the priors goes completely wrong which again can
>happen when you have very inhomogenous input data. This is not
>high-field data by any chance?
>> Is it possible that you didn’t set the origin before preprocessing the
>> data? In our experience the unified segmentation approach is pretty
>> vulnerable to end up with such deformed results if you don’t give the
>> program a reasonable starting point. Assuming that the data quality is
>> o.k., my best guess would be: reset the origin to AC, rerun data
>> preprocessing for these subjects, and you’re done.
>This may help, too. Christian had implemented an automated determination
>of the center of mass of an image in order to improve the starting
>estimates in a beta-version of the 5.1 toolbox but I don't know if it is
I also assume that spatial registration was not correct. You may try the new VBM5.1 Toolbox,
which is still a beta version with many new (and not yet documented) features (e.g. correction for
non-isotropic smoothness, segmentation without priors, pre-registration using center of mass):
>> The ages of the subjects are 1-3 years old.
>there is no reference data for such subjects yet that I am aware of. We
>(Christian, the Cincinnati IRC group and myself) have two abstracts at
>HBM where we investigate a prior-less segmentation for datasets from
>infants. In effect, the segmentation is done as in spm5 but for writing
>out the results only tissue intensity information is used, disregarding
>the influence from the priors. It does make the segmentation somewhat
>more vulnerable, especially w.r.t. inhomogeneity, but it seems to
>considerably improve segmentation results on "unusual" datasets. Not
>sure if the updated 5.1 toolbox is already publicly available but you
>could ask Christian.
Thanks for the push Marko! As he already mentioned, it might be helpful for children/infant data
to try the new prior-free segmentation approach, which can be found in the extended options.
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Philosophenweg 3, D-07743 Jena, Germany
Tel: ++49-3641-935805 Fax: ++49-3641-935280
e-mail: [log in to unmask]
>> The segmented images come out like this (please see attached jpg of
>> brains with modulation, segmentation and normalization applied; the left
>> brain is the one with the problem, the right brain is for comparison).
>Ah, good to know ;) But again, this is a problem with matching the
>priors and thus effectively a normalization problem. Note the oblique
>cutoff at the upper left side, this is where either the FOV or the
>tissue boundary of the input image went. Try the suggestions above and
>let us know how you fared.
>Marko Wilke (Dr.med./M.D.)
> [log in to unmask]
>Universitäts-Kinderklinik University Children's Hospital
>Abt. III (Neuropädiatrie) Dept. III (Pediatric neurology)
> Hoppe-Seyler-Str. 1, D - 72076 Tübingen
>Tel.: (+49) 07071 29-83416 Fax: (+49) 07071 29-5473