Dear experts,
I recently tried using the "segment" function in spm12 on extremely low dose CT images and I was impressed by how well it works, despite the terrible image quality. Obviously the grey matter segmentation isn't resolute at all, gyry aren't identified. You only get a single large thing including all of the area where grey matter is present. But it does find the borders of this area correctly, as compared to MRI. And this results in a very good normalization!
I've done some preliminar tests on 10 cases and the algorithm worked more than perfectly in 8 cases and failed markedly in 2 (segmenting as bone also part of the grey matter and of the outside tissues), resulting in a wrong normalization.
Therefore, as in a CT images one could provide very good priors for the intensities of bone, air, CSF and outside tissue even in extremely noisy situations I was wondering... Is there an easy way to do this? I'd think it could easily end up with a 100% success rate!
Thank you,
Luca
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