Dear John,
I've tried changing many options this morning, and got no improvements in patients where I had previously experienced segmentation imprecisions. Whatever setting I used I got the exact same result. Setting the number of gaussians to non parametric, instead, resulted in the log likelihood graph going like a roller coaster and the algorithm failing (everything segmented as c5!).
However, in the end I managed to get it working perfectly for all patients (apparently... more validation needed). What was happening, where I observed imprecisions, was that the c4 instead of containing only the bone, contained all the extra-brain tissue (eyes, skin, nose cavities) and c5 included lots of outside low-intensity objects (head holder cushion, straps to keep the patient still etc...). Therefore I gave in input to the algorithm a new image where all the intensities HU<-350 where set to -1024. Now, apparently, it works for every patient!
Thank you for you help!
Luca
________________________________________
Da: John Ashburner <[log in to unmask]>
Inviato: mercoledì 16 novembre 2016 19.03.41
A: PRESOTTO LUCA
Cc: [log in to unmask]
Oggetto: Re: [SPM] Tweaking segment for CT images
I'm not really sure what would help as I haven't built up so much intuition about the behaviour with CT. One thing that I added to the algorithm, which was originally done to try to help CT segment a CT dataset I was looking at, was to include a non-parametric option. If you change the number of Gaussians of any of the clusters to non-parametric, then instead of Gaussians, it will use a histogram to represent tissue intensity distributions. This may help - but I can't guarantee it.
Best regards,
-John
On 16 November 2016 at 16:24, PRESOTTO LUCA <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Thank you!
I'm fully aware of the problems regarding the CSF between gray matter and the skull. Especially in these low-dose CTs the partial volume effect is very intense in this area.
In your opinion, would it help the segmentation if I added a second channel, where I gave in input a segmented image of only the bone? (as my percentage of patients where the segmentation fails generally have the c4 class completely wrong)
Best regards,
Luca
________________________________________
Da: John Ashburner <[log in to unmask]<mailto:[log in to unmask]>>
Inviato: mercoledì 16 novembre 2016 15.06.21<tel:2016%2015.06.21>
A: PRESOTTO LUCA
Cc: [log in to unmask]<mailto:[log in to unmask]>
Oggetto: Re: [SPM] Tweaking segment for CT images
This is something I've been thinking about, but it hasn't been implemented in SPM yet. Preliminary work for learning tissue intensity priors was done in:
Blaiotta C, Cardoso MJ, Ashburner J. Variational inference for medical image segmentation. Computer Vision and Image Understanding. 2016 Apr 11.
As most people use T1-weighted scans, having intensity priors for these should bring some additional robustness. Much more informative priors should be achieved for CT, as Hounsfield units should not vary from scanner to scanner (unlike the contrast in T1-w images). Problems do arise with thick sliced CT though, because partial volume effects often mean that CSF outside the brain is not properly visible (through mixing with the much higher intensities from the skull). Major problems can be encountered because this CSF and that in the ventricles is supposed to have the same intensity distribution, when in reality it doesn't.
Best regards,
-John
On 16 November 2016 at 13:11, PRESOTTO LUCA <[log in to unmask]<mailto:[log in to unmask]><mailto:[log in to unmask]<mailto:[log in to unmask]>>> wrote:
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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