Hi Pablo,
I have one patient with damaged white matter, and SPM8 tissue segmentation
is not good if using default configuration. However, when I added the brain
mask without these bad regions, SPM8 is happy to work. Maybe you can try
this approach if you don't want to classify the lesion as another tissue
class.
Wayne
On 09/09/10 6:14 AM, "Jonathan Peelle" <[log in to unmask]> wrote:
> Hi Pablo
>> I am currently working on a study of patients who went through surgery and
>> had removed regions of their brain. SPM8 does a good job on segmenting and
>> normalizing all the patients but one, who has a big resection. In this
>> patient segmention is not done properly and I cannot find out why.SPM does
>> agreat job with other patients with a resection as big as this one´s.
>> żIs there another way of normalizing for particular cases like this?
>
> The problem is likely the large mismatch between the expected tissue
> classes (i.e., the tissue probability maps SPM uses to aid in
> segmentation, which are based on normal brains) and the actual anatomy
> of your patient's brain. I.e., the priors are in this case providing
> wrong information because the patient's brain is not normal.
>
> I haven't tried this yet myself, but you may want to look at this
> paper from Seghier et al. in which they included an extra tissue class
> to help model lesioned areas:
>
> Seghier et al. (2008) Lesion identification using unified
> segmentation-normalisation models and fuzzy clustering. NeuroImage 41,
> 1253-1266.
> http://dx.doi.org/10.1016/j.neuroimage.2008.03.028
>
> Even if you don't use the same "lesion identification" process, I
> wonder if being able to model the lesion as its own tissue class would
> give you a more accurate segmentation. Maybe someone else on the list
> has tried this?
>
> Good luck!
>
> Jonathan
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