Thank you for the information.
Have the segmentation classification results with FAST 3 been obtained
with PVE or hard segmentation?
In my experiments with FAST 4, I see that the GM volume measurement
with hard segmentation is consistently less than the PVE measurement
on the same subject.
On another note, I see on my experiments that GM/WM is closer to 1.15.
--
Murat
On Mon, Jun 22, 2009 at 5:14 PM, Jeremy Gray<[log in to unmask]> wrote:
> thanks Mark.
>
> I went back and used fast 4.1 to segment the data I had previously seg'd
> using fast 3, to compare versions head to head. here's my conclusions,
> posted here in case anyone else cares about this issue down the road.
>
> conclusions: there are strong similarities and differences between
> versions, in terms of results.
>
> - the fast 3 results are effectively the same as fast 4.1 if you only care
> about within-study variation. fast 3 results correlate very strongly with
> fast 4.1 results (r's > .98). so, if you have healthy subjects and only care
> about relative effects, the version of fast probably does not matter.
>
> - fast 4.1 gives me systematically smaller estimates for grey matter, larger
> estimates for white matter and CSF, and smaller G / W ratio. I see this
> pattern in every subject, for 121 subjects (= a mix of male and female
> healthy adults, age 18-40.) if you care about actual volume estimates (like
> how many cc's is a given human's brain), this will matter.
>
> - if anything the 4.1 grey and white estimates are probably more accurate
> (at least in relative terms), based on the wider literature. I was thinking
> of G/W ratio being ~1.6 extrapolating from Zhang & Sejknowski 2000 PNAS
> (their fig 2), but other reports put the G/W ratio at 1.35 for women and
> 1.27 for men (= Allen et al 2003 NeuroImage). using fast 4.1 I get a G/W
> ratio of 1.30 in 121 subjects (mixed male and female). my other dataset with
> G/W ratio of 1.14 was only men, so maybe the 1.14 ratio is not so bad (still
> seems a tad low versus 1.27, but whatever, maybe scanner effects are
> possible too).
>
> --Jeremy
>
>
> gory details:
>
> starting from T1 images in 121 subjects, I get a correlation of .986 (gray)
> and .987 (white) for volume when processed using fast 3.x versus fast 4.1
> (i.e., correlating estimates from f3 with f4). the fast 4.1 estimates are
> systematically smaller in terms of gray volume (83 - 147 cc smaller =
> smaller in every single subject, mean 110 cc smaller). conversely, white
> matter is systematically larger when estimated by fast 4.1, by 37 to 83 cc
> (larger in every single subject, mean 61 cc larger).
>
> so overall brain volume (gray + white) and the gray / white ratio is less,
> in every single subject, when using fast 4.1. the G / W ratio drops from
> 1.67 (fast 3.x) to 1.30 (fast 4.1). (in my other data set the ratio was 1.14
> in men only using fast 4, so some of the differences in the G/W ratio
> appears to be the sample / scanner, but there's still something else
> systematic going on with fast.)
>
>> What do the differences between the pve images look like between the
>> methods?
>> That is, take the grey matter PVE in both cases and do the subtraction of
>> them,
>> then view this in FSLView. Does it look unusual? Does the difference
>> tend to
>> concentrate in some areas (e.g. deep-grey structures, border of GM and
>> CSF, etc.)?
>> Or is there just a general bias one way across the whole image?
>
> I don't have pve images from the FAST 3 analyses, but do still have the
> segmentation classification images with all three tissue types. from these I
> isolated gray matter using fslmath functions, and then subtracted the fast 4
> gray matter pve image from the fast 3 gray matter image. I only did this for
> one subject. I am not sure what to look for exactly, but it does not look
> obviously odd: lots of small discrepancies spread all over the brain, both
> positive and negative differences, and zero in places that should be white
> matter or ventricles.
>
>
>
>
>
>
>
>> On 21 Jun 2009, at 18:53, Jeremy Gray wrote:
>>
>>> Hi all,
>>>
>>> I have a question about how FAST 4.1 differs from earlier versions, esp
>>> 3.x. I get quite different results in two large samples, in terms of grey
>>> matter to white matter ratio (ratio = 1.14 from FAST 4.1, vs ratio = 1.67
>>> from 3.x). This suggests big difference(s) in classification between
>>> versions. there are many differences between versions, obviously, but I did
>>> not expect these to impact the ratio much if at all. I think a ratio of 1.6
>>> is much more in line with the literature.
>>>
>>> any ideas what's going on, or how to track it down? I searched the
>>> archives but did not see anything relevant, sorry if I missed it.
>>>
>>> thanks in advance,
>>>
>>> --Jeremy
>>>
>>> gory details:
>>> I ran T1 images from 114 subjects through bet (and checked that they look
>>> reasonable after bet). I then used FAST 4.1 (on CentOS 5.3, 64-bit) to
>>> segment them using defaults:
>>>
>>> fast -t 1 -o <image> <image>
>>>
>>> I looked at some of the resulting pve_* images and they look reasonable
>>> in fslview. from the pve's I get CSF, grey, and white matter volumes in mm^3
>>> using fslstats and bc, as described on the FAST web page
>>> (http://www.fmrib.ox.ac.uk/fsl/fast4/index.html)
>>>
>>> my intuition is that the grey to white matter ratio should be pretty
>>> robust against scanner and sample differences (but maybe that's wrong). in
>>> the sample I'm using FAST 4.1 for, the ratio of GM : WM is 1.14. This is
>>> markedly lower that the G/W ratio of 1.67 I got in another large sample
>>> (different scanner & subjects, but I don't think that should have a huge
>>> impact on the ratio). this was using FAST 3.x (again on T1 images run
>>> through bet). so think that the version of FAST is where the difference is,
>>> and that in my hands 4.1 is not doing what it should.
>>>
>>> also, there seems to be more CSF when using FAST 4.1 (~25% of intra-skull
>>> volume, vs ~15% with 3.x).
>>>
>>> any ideas on how to best track down what is going on (esp with 4.1)?
>>>
>
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