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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)? >>>> >>