External Email - Use Caution
Dear FreeSurfer Developers,
I would like to run the Hippocampus subfields segmentation on Freesurfer 7.1.1.
I am working on a HCP cluster with CentOS 7 (Core). Before running the
segmentation on my data, I first tried to run the segmentation on your fully
recon-ed subject Bert typing 'segmentHA_T1.sh bert'. Unfortunately the process
stopped after one minute and it reported the following error:
Out of memory. Type HELP MEMORY for your options.
Error in segmentSubjectT1_autoEstimateAlveusML (line 210)
I've searched the list and no similar errors have been reported. Also, I have
attached the hippocampal-subfields-T1.log file in case it's of any use.
1) FreeSurfer version: freesurfer-linux-centos7_x86_64-7.1.1-20200723-8b40551
2) Platform: CentOS Linux 7 (Core)
3) Kernel: Linux 4.19.94-300.el7.x86_64
Does anyone have any suggestions on how this can be fixed?
Thank very much you in advance!
Kind Regards,
Alessio
#--------------------------------------------
#@# Hippocampal Subfields processing (T1) left Thu Dec 17 12:05:21 CET 2020
------------------------------------------
Setting up environment variables
---
LD_LIBRARY_PATH is
.:/opt/freesurfer/7.1.1/MCRv84//runtime/glnxa64:/opt/freesurfer/7.1.1/MCRv84//bin/glnxa64:/opt/freesurfer/7.1.1/MCRv84//sys/os/glnxa64:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/server:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64/client:/opt/freesurfer/7.1.1/MCRv84//sys/java/jre/glnxa64/jre/lib/amd64:/opt/R/3.5.1/lib64/R/lib:/opt/cluster/lib:/opt/cluster/external/p7zip-16.02/lib/p7zip
Registering imageDump.mgz to hippocampal mask from ASEG
7.1.1
--mov: Using imageDump.mgz as movable/source volume.
--dst: Using
/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz
as target volume.
--lta: Output transform as trash.lta .
--mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz !
--sat: Using saturation 50 in M-estimator!
reading source 'imageDump.mgz'...
reading target
'/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'...
Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE )
Type Source : 0 Type Target : 3 ensure both FLOAT (3)
Reordering axes in mov to better fit dst... ( -1 3 -2 )
Determinant after swap : 0.015625
Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241)
Dst: (1, 1, 1)mm and dim (38, 39, 63)
Asserting both images: 1mm isotropic
- reslicing Mov ...
-- changing data type from 0 to 3 (noscale = 0)...
-- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels.
-- Resampled: (1, 1, 1)mm and (38, 39, 63) voxels.
-- Reslicing using cubic bspline
MRItoBSpline degree 3
- no Dst reslice necessary
Registration::computeMultiresRegistration
- computing centroids
- computing initial transform
-- using translation info
- Get Gaussian Pyramid Limits ( min size: 16 max size: -1 )
- Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 )
- Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 )
- initial transform:
Ti = [ ...
1.0000000000000 0 0 -0.3773093080253
0 1.0000000000000 0 2.1148246252257
0 0 1.0000000000000 -2.0733903827146
0 0 0 1.0000000000000 ]
- initial iscale: Ii =1
Resolution: 1 S( 19 19 31 ) T( 19 19 31 )
Iteration(f): 1
-- diff. to prev. transform: 13.1875
Iteration(f): 2
-- diff. to prev. transform: 6.80473
Iteration(f): 3
-- diff. to prev. transform: 6.6167
Iteration(f): 4
-- diff. to prev. transform: 6.78298
Iteration(f): 5
-- diff. to prev. transform: 0.625873 max it: 5 reached!
Resolution: 0 S( 38 39 63 ) T( 38 39 63 )
Iteration(f): 1
-- diff. to prev. transform: 6.47442
Iteration(f): 2
-- diff. to prev. transform: 1.63452
Iteration(f): 3
-- diff. to prev. transform: 0.300624
Iteration(f): 4
-- diff. to prev. transform: 0.107388
Iteration(f): 5
-- diff. to prev. transform: 0.0250386 max it: 5 reached!
- final transform:
Tf = [ ...
0.9905498265002 -0.0558283673261 -0.1252766323867 5.6450814796323
0.1003642556845 0.9175875714603 0.3846557745341 -12.9958814513727
0.0934775769976 -0.3935940066912 0.9145193822415 7.3531774744762
0 0 0 1.0000000000000 ]
- final iscale: If = 1
**********************************************************
*
* WARNING: Registration did not converge in 5 steps!
* Problem might be ill posed.
* Please inspect output manually!
*
**********************************************************
Final Transform:
Adjusting final transform due to initial resampling (voxel or size changes) ...
M = [ ...
-0.2476374566250 -0.0313191580967 0.0139570918315 38.5003166492308
-0.0250910639211 0.0961639436335 -0.2293968928651 20.0770216669627
-0.0233693942494 0.2286298455604 0.0983985016728 -0.5533689445097
0 0 0 1.0000000000000 ]
Determinant : -0.015625
writing output transformation to trash.lta ...
converting VOX to RAS and saving RAS2RAS...
mapmovhdr: Changing vox2ras MOV header (to map to DST) ...
To check aligned result, run:
freeview -v
/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz
imageDump_coregistered.mgz
Registration took 0 minutes and 1 seconds.
Thank you for using RobustRegister!
If you find it useful and use it for a publication, please cite:
Highly Accurate Inverse Consistent Registration: A Robust Approach
M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010.
http://dx.doi.org/10.1016/j.neuroimage.2010.07.020
http://reuter.mit.edu/papers/reuter-robreg10.pdf
7.1.1
--mov: Using imageDump.mgz as movable/source volume.
--dst: Using
/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz
as target volume.
--lta: Output transform as trash.lta .
--mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz !
--affine: Enabling affine transform!
--sat: Using saturation 50 in M-estimator!
reading source 'imageDump.mgz'...
reading target
'/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'...
Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE )
Type Source : 0 Type Target : 3 ensure both FLOAT (3)
Reordering axes in mov to better fit dst... ( -1 3 -2 )
Determinant after swap : 0.015625
Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241)
Dst: (1, 1, 1)mm and dim (38, 39, 63)
Asserting both images: 1mm isotropic
- reslicing Mov ...
-- changing data type from 0 to 3 (noscale = 0)...
-- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels.
-- Resampled: (1, 1, 1)mm and (38, 39, 63) voxels.
-- Reslicing using cubic bspline
MRItoBSpline degree 3
- no Dst reslice necessary
Registration::computeMultiresRegistration
- computing centroids
- computing initial transform
-- using translation info
- Get Gaussian Pyramid Limits ( min size: 16 max size: -1 )
- Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 )
- Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 )
- initial transform:
Ti = [ ...
1.0000000000000 0 0 -0.3773081857769
0 1.0000000000000 0 2.1147372976911
0 0 1.0000000000000 -2.0733392207393
0 0 0 1.0000000000000 ]
- initial iscale: Ii =1
Resolution: 1 S( 19 19 31 ) T( 19 19 31 )
Iteration(f): 1
-- diff. to prev. transform: 13.4057
Iteration(f): 2
-- diff. to prev. transform: 6.82866
Iteration(f): 3
-- diff. to prev. transform: 9.88716
Iteration(f): 4
-- diff. to prev. transform: 10.7689
Iteration(f): 5
-- diff. to prev. transform: 3.25195 max it: 5 reached!
Resolution: 0 S( 38 39 63 ) T( 38 39 63 )
Iteration(f): 1
-- diff. to prev. transform: 10.2673
Iteration(f): 2
-- diff. to prev. transform: 2.17054
Iteration(f): 3
-- diff. to prev. transform: 0.806598
Iteration(f): 4
-- diff. to prev. transform: 0.338381
Iteration(f): 5
-- diff. to prev. transform: 0.104875 max it: 5 reached!
- final transform:
Tf = [ ...
1.2030223237189 0.1884187660644 -0.0167932422667 -7.4790282553433
0.1560810568266 1.0236440479534 0.4645717799507 -19.5773333553553
0.2438450826014 -0.5807477383867 0.9710372375638 5.5635008355781
0 0 0 1.0000000000000 ]
- final iscale: If = 1
**********************************************************
*
* WARNING: Registration did not converge in 5 steps!
* Problem might be ill posed.
* Please inspect output manually!
*
**********************************************************
Final Transform:
Adjusting final transform due to initial resampling (voxel or size changes) ...
M = [ ...
-0.3007555803217 -0.0041983073249 -0.0471046758039 40.7127838486195
-0.0390202577973 0.1161429175878 -0.2559109503930 18.9165458088794
-0.0609612673877 0.2427592519668 0.1451869008898 -2.9024121066671
0 0 0 1.0000000000000 ]
Determinant : -0.0237324
Decompose into Rot * Shear * Scale :
Rot = [ ...
-0.9890542235170 -0.1314605763023 -0.0670064162713
-0.0031820392005 0.4730167673437 -0.8810476788677
-0.1475181940297 0.8711907108607 0.4682575442428 ]
Shear = [ ...
1.0000000000000 -0.1199668004135 0.0876086668809
-0.1044704729898 1.0000000000000 0.0392019164457
0.0847597162801 0.0435529158645 1.0000000000000 ]
Scale = diag([ 0.3065806370242 0.2669790646166 0.2966109260227 ])
writing output transformation to trash.lta ...
converting VOX to RAS and saving RAS2RAS...
mapmovhdr: Changing vox2ras MOV header (to map to DST) ...
To check aligned result, run:
freeview -v
/home/cogaff/alepro/subjects/betty/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz
imageDump_coregistered.mgz
Registration took 0 minutes and 1 seconds.
Thank you for using RobustRegister!
If you find it useful and use it for a publication, please cite:
Highly Accurate Inverse Consistent Registration: A Robust Approach
M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010.
http://dx.doi.org/10.1016/j.neuroimage.2010.07.020
http://reuter.mit.edu/papers/reuter-robreg10.pdf
Out of memory. Type HELP MEMORY for your options.
Error in segmentSubjectT1_autoEstimateAlveusML (line 210)
MATLAB:nomem
@#@FSTIME 2020:12:17:12:05:21 run_segmentSubjectT1_autoEstimateAlveusML.sh N
14 e 13.45 S 1.20 U 8.73 P 73% M 849776 F 0 R 340169 W 0 c 2552 w 6922 I 262320
O 216696 L 12.17 9.21 10.22
@#@FSLOADPOST 2020:12:17:12:05:34 run_segmentSubjectT1_autoEstimateAlveusML.sh
N 14 11.08 9.11 10.17
Linux mentat004.dccn.nl 4.19.94-300.el7.x86_64 #1 SMP Thu Jan 9 16:15:13 UTC
2020 x86_64 x86_64 x86_64 GNU/Linux
T1 hippocampal subfields exited with ERRORS at Thu Dec 17 12:05:35 CET 2020_______________________________________________
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
