Re: [caret-users] Hi res recon
Dear Donna, Colin, and David, Thank you all for the input. Turning off automatic error correction helped in significantly cutting off processing time. I managed to reconstruct with additional manual labour. It worked reasonably well. The topological errors were not that many, especially when we're only interested in a certain roi. Thanks again everyone. Aditya David Van Essen vanes...@wustl.edu wrote: Alternatively, you may find that FreeSurfer will work better for your current needs. Matt Glasser and others have gotten it to work reasonably well on macaque structural images. David On Oct 15, 2014, at 2:38 AM, Donna Dierker do...@brainvis.wustl.edu wrote: Hi Aditya, On monkeys, yes. Humans, no. The SureFit algorithm that is in Caret's segmentation feature was designed for cubic 1mm human data. It worked reasonably well on higher res monkey data, but some of the subroutines will likely break on higher res human data (e.g., disconnecting eye, skull, hindbrain). I'd turn all error correction features off and sanity check the initial segmentation. If the skull, eye, or hindbrain is still connected, then resolving that issue should precede the error correction steps. Unfortunately, that will likely take some work. Donna On Oct 14, 2014, at 5:25 AM, Dr. Aditya Tri Hernowo, Ph.D adityatrihern...@gmail.com wrote: Dear users experts, Does anyone have any experience with reconstructing the cortex on 0.5mm resolution T1 images? I am still having problems with the very long time it takes to perform automatic error correction (more than 3 hours before the software finally crashed). Regards, Aditya Hernowo ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
[caret-users] Hi res recon
Dear users experts, Does anyone have any experience with reconstructing the cortex on 0.5mm resolution T1 images? I am still having problems with the very long time it takes to perform automatic error correction (more than 3 hours before the software finally crashed). Regards, Aditya Hernowo ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
Re: [caret-users] Hi res recon
Hi Aditya, On monkeys, yes. Humans, no. The SureFit algorithm that is in Caret's segmentation feature was designed for cubic 1mm human data. It worked reasonably well on higher res monkey data, but some of the subroutines will likely break on higher res human data (e.g., disconnecting eye, skull, hindbrain). I'd turn all error correction features off and sanity check the initial segmentation. If the skull, eye, or hindbrain is still connected, then resolving that issue should precede the error correction steps. Unfortunately, that will likely take some work. Donna On Oct 14, 2014, at 5:25 AM, Dr. Aditya Tri Hernowo, Ph.D adityatrihern...@gmail.com wrote: Dear users experts, Does anyone have any experience with reconstructing the cortex on 0.5mm resolution T1 images? I am still having problems with the very long time it takes to perform automatic error correction (more than 3 hours before the software finally crashed). Regards, Aditya Hernowo ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
Re: [caret-users] Hi res recon
Alternatively, you may find that FreeSurfer will work better for your current needs. Matt Glasser and others have gotten it to work reasonably well on macaque structural images. David On Oct 15, 2014, at 2:38 AM, Donna Dierker do...@brainvis.wustl.edu wrote: Hi Aditya, On monkeys, yes. Humans, no. The SureFit algorithm that is in Caret's segmentation feature was designed for cubic 1mm human data. It worked reasonably well on higher res monkey data, but some of the subroutines will likely break on higher res human data (e.g., disconnecting eye, skull, hindbrain). I'd turn all error correction features off and sanity check the initial segmentation. If the skull, eye, or hindbrain is still connected, then resolving that issue should precede the error correction steps. Unfortunately, that will likely take some work. Donna On Oct 14, 2014, at 5:25 AM, Dr. Aditya Tri Hernowo, Ph.D adityatrihern...@gmail.com wrote: Dear users experts, Does anyone have any experience with reconstructing the cortex on 0.5mm resolution T1 images? I am still having problems with the very long time it takes to perform automatic error correction (more than 3 hours before the software finally crashed). Regards, Aditya Hernowo ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
[caret-users] Hi,
How to make the surface transparent? If you can provide some advice for making ROI and CONNECTION on the transparent brain surface, it will be much more helpful to me. In fact, i want to plot the brain map used in the paper Prediction of individual brain maturity using fMRIhttp://www.sciencemag.org/content/329/5997/1358.short, can some body did this work? Thank you very much. -- Sincerely LongfeiSu Ph. D. Candidate, College of Mechatronics and Automation, National University of Defense Technology, Add: No.47 Yanwachizheng Street, Changsha, Hunan, 410073, China, PR ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
[caret-users] Hi,
I have generated two sample t-test results spmT_0001.hdr/img, and i tried to map it to the surface. The result was strange, the left and the right hemisphere were activated completely the same. In fact, the left and the right hemisphere were activated different. How can i get the correct results when the left and the right hemisphere were activated differently? -- Sincerely LongfeiSu Ph. D. Candidate, College of Mechatronics and Automation, National University of Defense Technology, Add: No.47 Yanwachizheng Street, Changsha, Hunan, 410073, China, PR ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
Re: [caret-users] Hi,
This could happen easily if you are using Caret5 and the same visualization specification for both hemispheres. If you have both metric files loaded, but the same metric overlay is set to apply to All surfaces (see top of metric selection page selection on D/C menu). Are you mapping to PALS_B12 or fs_LR? September 2006 tutorial has separate LEFT and RIGHT standard scenes visualization specifications. I confess I still use them, to keep myself un-confused. If you do have both the LEFT and RIGHT mapping metric columns loaded in the same session, try toggling from one to the other. Are they still identical? (Alternatively, you can press the H button on the D/C metric selection menu for each column, and see if they are identical.) If they really are identical, then something went wrong during the mapping (target surface selection, possibly). But let's start with these checks before building a whole tree of possibilities. ;-) On 11/08/2013 10:33 AM, ??? wrote: I have generated two sample t-test results spmT_0001.hdr/img, and i tried to map it to the surface. The result was strange, the left and the right hemisphere were activated completely the same. In fact, the left and the right hemisphere were activated different. How can i get the correct results when the left and the right hemisphere were activated differently? -- Sincerely Longfei Su Ph. D. Candidate, College of Mechatronics and Automation, National University of Defense Technology, Add: No.47 Yanwachizheng Street, Changsha, Hunan, 410073, China, PR ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
[caret-users] hi res macaque
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 ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
Re: [caret-users] hi res macaque
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 cm...@sussex.ac.uk mailto:cm...@sussex.ac.uk 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 ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users ___ caret-users mailing list caret-users@brainvis.wustl.edu http://brainvis.wustl.edu/mailman/listinfo/caret-users
Re: [caret-users] hi res macaque
Sorry I don't have time to read this more carefully, but I'm swamped this week. But the quick scan leads me to believe this is an issue with you trying to segment monkey data at a resolution above 0.5mm. The problem is that some of the routines (e.g., especially eye/skull/hindbrain removal) depend on the number of slices away from the AC something is. If the number of slices is twice what it expects, it won't work. You can turn off eye/skull removal, but as you already know, de-checking hindbrain just makes it fail. Caret's segmentation has its limits, and this is now where our development effort is focused these days. Sorry. I know some people have gotten Freesurfer to segment monkey data, but I suspect there are tricks/tweaks, and I do not know them. I don't know how the talairach.xfm stuff (and that which depends on it) works, for example. Obviously MNI305 won't work as a target. On Oct 25, 2011, at 7:54 AM, wolf zinke wrote: 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 cm...@sussex.ac.uk 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
Re: [caret-users] hi res macaque
this is now where our development effort is focused these days should have read this is NOT where our development effort is focused these days On Oct 25, 2011, at 9:33 AM, Donna Dierker wrote: Sorry I don't have time to read this more carefully, but I'm swamped this week. But the quick scan leads me to believe this is an issue with you trying to segment monkey data at a resolution above 0.5mm. The problem is that some of the routines (e.g., especially eye/skull/hindbrain removal) depend on the number of slices away from the AC something is. If the number of slices is twice what it expects, it won't work. You can turn off eye/skull removal, but as you already know, de-checking hindbrain just makes it fail. Caret's segmentation has its limits, and this is now where our development effort is focused these days. Sorry. I know some people have gotten Freesurfer to segment monkey data, but I suspect there are tricks/tweaks, and I do not know them. I don't know how the talairach.xfm stuff (and that which depends on it) works, for example. Obviously MNI305 won't work as a target. On Oct 25, 2011, at 7:54 AM, wolf zinke wrote: 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 cm...@sussex.ac.uk 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
