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Dear Mohamed Thank you very much for replying. You could reorient your images first (e.g. coregister them to the MNI-T1 image of SPM) and then rerun the segmentation tool (your version 1 below)... I coregistered the anatomy of the second session with the one of the first session before I run the segmentation! Regarding your point (3), I can send you my toolbox if you would like to give it a go... I will like very much to try, if it is not a problem for you to send me the toolbox. All the best, Natalia On 27/09/2011 13:24, Natalia Estévez wrote: > > Dear John and all other experts > In the meantime I test two ways of segmentation using the data of a > stroke patient. Attached you can find some images. > > 1. version: I masked the lesion using the anatomy of the first measurement. > I enter this mask during the segmentation of the anatomy for the first > and for the second session (sessions are 2 months apart). The lesion > was properly masked out during the first measure, so that there are > not gray matter in this area (image: P04_FM_2mess_SegMasked). However > when seeing the gray matter results of the second session (image: > P04_FM_3mess_SegMasked) you can see gray matter in the region of the > lesion. How can this be possible? > > 2. version: as Christine propose I used the segmentation procedure of > the VBM8-toolbox. The gray matter results shows still some gray matter > in the region of the lesion (image:P04_FM_2mess_SegVBM8), while the > results in the second session seems to be much better > (image:P04_FM_3mess_SegVBM8). Could the strange results during the > first session be due to the patient movement? When you look at the > anatomy of the first measurement (image:rP04_FM_2mess_Anatomy), this > image is not so nice compare to the anatomy in the second measurement (image:rP04_FM_3mess_Anatomy). > > I read the paper of Seghier you recommend me but I couldn't see that > there was a link to get this additional tissue class or some kind of > toolbox, so that I can test this segmentation procedure. Is there anything? > > For your help I thank you very much. > All the best, > Natalia > > -----Ursprüngliche Nachricht----- > Von: Natalia Estévez [mailto:[log in to unmask]] > Gesendet: Mittwoch, 21. September 2011 17:23 > An: [log in to unmask] > Betreff: WG: [SPM] Dartel template for normalizing function image > comment > > > Dear John > Thank you very much for your answer. I will have a look at the paper > and see if it helps. > It is probably a stupid question, but in case that I have to mask the > lesion out, can I still use Dartel to process my data (functional and anatomical)? > Or is it only possible to use Dartel when you have the whole brain? > > I thank you very much again for your help. > All the best, > Natalia > > -----Ursprüngliche Nachricht----- > Von: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] > Im Auftrag von John Ashburner > Gesendet: Mittwoch, 21. September 2011 16:49 > An: [log in to unmask] > Betreff: Re: [SPM] Dartel template for normalizing function image > comment > > Adding to some of Jonathan's comments.... > >> For structural image analysis, you would just normalize all of the >> rc1* (or rc2*, for white matter) files to MNI space, and conduct your >> statistics on those. > Results should be marginally better if the rc1 and rc2 images are used > for the estimation stage, and the deformations applied to the c1 and > c2. This saves an additional resampling of the data. > >>> 2. I'm analyzing data of stroke patients and when I use the "New >>> Segment" option the lesion is also shown as gray matter and some >>> parts of the lesion as white matter. But this happens with the > segmentation too. >>> Attached you can find a picture, the first two images are the c1 and >>> c2 after "Segmentation". The next two images are c1 and c2 after >>> "New > Segment" >>> and the last image is the anatomy of the patient, where you can see >>> the lesion. Can these files be used or is it maybe better to mask >>> out the lesion? >> I did not see the attachment, so I can't comment on your particular >> case. Also, I have not worked with stroke patient data before, so >> hopefully someone else can comment more knowledgeably. However, it >> does not surprise me that segmentations might fail (or be a bit odd) >> on these images, because they do not conform to what the segmentation >> algorithm "expects" (based on tissue probability maps). You may want >> to have a look at Seghier et al. (2008), who found that including an >> additional tissue class to model the lesion was successful. >> Otherwise, I would think that masking out the lesion is the best >> approach (in the case where the standard approach is not working). >> >> Seghier ML, Ramlackhansingh A, Crinion JT, Leff AP, Price CJ (2008) >> Lesion identification using unified segmentation-normalisation models >> and fuzzy clustering. NeuroImage 41:1253-1266. > The SPM segmentation model is pretty simple and assumes that brains > consist only of GM and WM. There is no part of the model for dealing > with regions affected by stroke. The behaviour in these regions will > depend on how the stroke changes the signal intensity. Typically, > affected WM will have an intensity closer to that of GM, so is more likely to be segmented as GM. > > To achieve a really good automatic segmentation of stroke lesions etc, > would need a useful dataset for training a segmentation algorithm. > Basically, lots of scans and some manually defined lesion masks. Even > then, it may be a bit tricky to make the approach generalise to MR > scans that differ from the training data. > > Best regards, > -John