Just to add to this, although I'm sure John will reply to this
eventually. I think part of the problem is that the prior for
'non-brain tissue' does not model non-brain tissue ie skull, blood
vessels, fat, air... very well and the original affine registration can
be poor because it registers the brain to the prior probability maps.
John's working on something that models these classes more explicitly.
I've personally worked around it by doing an initial affine registration
of the smoothed image to the MNI T1 atlas, and using that as an input to
spm_preproc for the affine.
Marko Wilke wrote:
> Hi Herve,
>> Using the unified segmentation in SPM5 (with the more recent updates
>> installed), we noticed that for a large proportion of subjects (30%),
>> belonging to the skull were misclassified into the CSF class (see
> I believe that the CSF segmentation has never been the focus of much
> attention ;) leading to the fact that the differentiation between
> external CSF spaces and skull tissue may not be otpimal.
>> Has anyone else noticed that and are there any ways to improve this
> I think what you could to is to focus on the ventricles by masking the
> CSF segmentation with a "center of brain mask", for example by eroding
> the default brain mask a couple of times. Alternatively, you could use
> a more aggressive cleaning/skull stripping routine, perhaps even
> before segmentation, which has been suggested to improve segmentation
> accuracy in the first place (a recent NeuroImage paper). Another thing
> perhaps worth looking into is the prior-less segmentation as
> implemented in Christian's toolbox, which may (or may not) improve
> results, again combined with a more aggressive cleaning approach.
> Other than that, no ideas.