Hi, sorry to repost. I didn't receive a reply before and was hoping someone could provide some advice.
After doing segmentation using CAT12 (on patients
with Alzheimer's Disease) and examining the GM segments afterwards I have
noticed that some WM appears to have been misclassified as GM in a small number
of participants (but in enough participants that I wouldn't want to discard
them readily).
This appears to be a problem that is not unique to CAT12, and that is sometimes encountered using a variety of segmentation methods.
1) I was wondering whether better segmentation of GM in these cases might be achieved via alternative settings in the CAT12 segmentation?
Might altering the denoising or local adaptive segmentation defaults help?
If so, what values would be recommended for this issue?
2) If I adjust the settings - I take it that I should segment the entire dataset again with those settings to be consistent?
3) Also I wondered how much of a concern misclassifications of GM regions would be practically? The misclassification mostly relates to periventricular regions, but is also present in some deeper WM.
In the model estimation stage a GM mask will be created - which could potentially remove the contribution of those misclassified regions in the statistical analysis(?). I could check the mask to see whether it includes those regions. Opinions would be appreciated.
Thanks,
Will
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