Hi,
I had a similiar question a while ago, that was related to your issues.
At this time there was no 64 bit version of caret available, and hence I
ran into memory troubles with a high resolution monkey file (do you use
the 64 bit version of caret?).
However, Donna Dierker gave me some pointers, why it would be
problematic to use a voxel size different from 0.5 mm. I am not sure, if
this is still true, but that might be a reason for your problem. I
myself had also the impression, that for the segmentation a resolution
of 0.25 mm would be very beneficial.
I hope that this information helps,
wolf
Hi,
Thanks for the clarification. Currently,I am running caret on the 0.5
mm resolution and it overall gives good results, but fails in
occipital regions. However, with your information I have a good reason
to stick to the 0.5 mm resolution - makes the manual correction faster
anyway.
thanks for the reply,
wolf
On 01/28/2010 04:03 PM, Donna Dierker wrote:
Setting aside the memory/64-bit question, there are assumptions built
into the SureFit algorithm that assume voxdims for monkeys around
0.5-0.75mm cubed. For example, there are routines in the hindbrain
removal that are based on number of *slices* from the AC, and if you
double the resolution, those will be off by a factor of two. In
short, it will fail.
Try downsampling to 0.5 and making sure you crop to left and right
hems. If the problems persist, upload your anatomical volume here:
http://pulvinar.wustl.edu/cgi-bin/upload.cgi
On 01/28/2010 07:30 AM, wolf zinke wrote:
Hi,
Sorry that I did not reply directly to this thread, but I did not
find any option for this reply.
Is there a reason why caret is not build for 64 bit systems? I tried
to run a segmentation on macaque data with 0.25 mm voxel size, hoping
to get better results due to the resoltion. However, Caret threw an
error about insufficient memory, which first puzzled me since the PC
got 32GB. But than I realized that due to the 32 bit, Caret is not
able to address more than 4GB of the RAM, right?
cheers,
wolf
On 25/10/11 08:30, Colin Reveley wrote:
I wonder if the bits of spine and the affine to to (deskulled,
upsampled) F99 are an issue.
I don't recall, but it's hugely likely I tried with rigid body too,
i.e. without the bit cut off at bottom.
On 25 October 2011 00:53, Colin Reveley <[email protected]
<mailto:[email protected]>> wrote:
Hello.
I have macaque data that is 0.25mm. I like that. I can do things
with it that are more than cosmetic.
The data was taken with a fancy brukker, and the contrast is very
good from the sequence used. so good I wonder if it's a problem
(it's a FLASH_MTR - it does correlate to T1 really closely, but
contrast GM-WM is clearer. and there may be differences.)
Thus far, I've been downsampling to 0.5 to make CARET surfaces.
I'm beginning to suspect that, for my project, there is profit in
a surface made at 0.25, with many nodes.
what I am interested in is the really quite small region (in
absolute terms) that was the subject of the paper by lewis and van
essen in 2000.
even though the F99 atlas does not have 300,000 nodes the paint,
border and metric data are scalable and my own data would indeed
support a hi-res surface, and benefit from it.
I've got RAM.
but I never managed to get far with 0.25.
segmentation fails with hindbrain at any resolution below 0.5.
I didn't mind. But now I think (I really do) I have a good reason
to seek surface construction directly from my structural data at
0.25mm.
So: is it possible? caret_command ... -res=X ?
my data is ex-vivo. And probably no more than 1% of nonGM or nonWM
voxels are nonzero. no ventricles. nothing. I did that. A mistake
maybe.
If I segment at 0.5, upsample to 0.25 and generate a surface with
my data it works. CARET can make the surface.
but segmentation does not work.
appreciate help.
Colin
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