Hi all,
hmmm, very interesting... a while ago we discussed whether fast can actually
run with 4 classes (or more) and I recall (possibly in error) that fast was
designed and effectively limited to 3 classes (or at least optimal at 3
classes rather than 4). Has something changed in the way that fast does the
initial k-means segmentation? Is this process an adaptive one for different
contrast images (eg, T1/T2)? Perhaps another processing stage has been
introduced? Are these results specific to a T2 contrast?
Best regards, Darren
----- Original Message -----
From: "Xavier Chitnis" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Wednesday, July 02, 2003 10:26 AM
Subject: Re: [FSL] mFAST
> Dear Steve,
>
> Thanks for taking a look at that image. The three class segmentation
> does produce a good result on that subject.
>
> However, when I started using mFAST on this dataset I initially
> segmented a subset of scans using the default settings. I found that the
> three class segmentation didn't work well, with generally a poor
> grey/CSF separation. I read on the FAST webpage that when segmenting T2
> images, it tended to work better with a 4th class for dark non-brain
> tissue. Hence I tried this and found that generally it produces
> excellent results. Out of 64 subjects, 56 have segmented very well with
> 4 tissue classes. It is a relatively small number where this approach
> hasn't worked.
>
> I have put on our webpage two new tar files (01_4CLASS and 01_3CLASS).
> They are the same subject segmented into 3 or 4 tissue classes (I
> apologise, the files are quite large...). The 4 class segmentation is
> very good, but the 3 class hasn't to my eye distinguished between grey
> matter and CSF.
>
> http://www.iop.kcl.ac.uk/iop/Departments/BioComp/BIAU/pickup.shtml
>
> Given that the 3 class segmentation may work for those images that fail
> the 4 class method (which works well for the vast majority), I am
> wondering whether it would be valid to combine data from scans segmented
> using two differing sets of parameters?
>
> As always, I am very grateful for any advice or suggestions
>
> Best wishes
>
> Xavier
>
>
>
> -----Original Message-----
> From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On
> Behalf Of Stephen Smith
> Sent: 01 July 2003 13:52
> To: [log in to unmask]
> Subject: Re: [FSL] mFAST
>
> Hi - I'm a little confused - your questions certainly made sense, but
> running mfast with the defaults on this dataset gave very nice results!
> I
> ran:
>
> mfast -s 2 -c 3 -od 27_m_pd 27_m_pd.hdr 27_m_t2.hdr
>
> does this not give good results for you?
>
> thanks :)
>
>
>
> On Mon, 30 Jun 2003, Xavier Chitnis wrote:
>
> > Dear All,
> >
> >
> >
> > I have been using mFAST to segment some dual-echo (proton density/T2)
> > images.
> >
> >
> >
> > Following the suggestions on the FSL webpage, I have been segmenting
> > them into 4 classes, using partial volume classification.
> >
> >
> >
> > This has worked on the majority of my images. However, the
> segmentation
> > is failing on around 10% of my data. The segmentation finds a white
> > matter class, and (what I term) a dura class consisting of non-brain
> > tissue left by BET. However, it fails to separate grey matter and CSF
> > accurately.
> >
> >
> >
> > I have found in this dataset that 3 class segmentation failed. I have
> > tried changing some of the options e.g. disabling bias field
> correction,
> > or going for 2D segmentation, however have been unable to improve
> > matters.
> >
> >
> >
> > I would be very grateful for any suggestions. I have put an example
> > subject's data (27_fse.tar.gz) at
> > http://www.iop.kcl.ac.uk/iop/Departments/BioComp/BIAU/pickup.shtml if
> > anyone would like to take a look at it.
> >
> >
> >
> > Many thanks
> >
> >
> >
> > Xavier Chitnis
> >
> >
> >
> >
> >
> > Neuroimaging Research Group
> >
> > Institute of Psychiatry, London
> >
> >
> >
> >
>
> Stephen M. Smith MA DPhil CEng MIEE
> Associate Director, FMRIB and Analysis Research Coordinator
>
> Oxford University Centre for Functional MRI of the Brain
> John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
> +44 (0) 1865 222726 (fax 222717)
>
> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve
>
|