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
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