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
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