thanks for your comments, Murat.
> Have the segmentation classification results with FAST 3 been
> obtained with PVE or hard segmentation?
I suspect I used hard segmentation with FAST 3 but am not positive.
(my interface to FAST was an older version of BioImage Suite, which
called FAST 3 for segmentation. I was not familiar with FAST at the
time, so was not paying attention to hard vs pve at the time. I don't
have pve images from that analysis, and the segmentation images I do
have look all or none, rather than graded like the pve images I get
from FAST 4. so I would guess that I did a 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.
interesting, GM volume is really LESS for hard seg (not more)? in my
data I'm seeing that GM vol is greater for (what I presume is) hard
seg in fast3, vs pve in fast4
> On another note, I see on my experiments that GM/WM is closer to 1.15.
this is nice to hear! thanks for checking.
--Jeremy
> --
> 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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