Dear experts,

I am running the hippocampal/amygdala segmentation on T1's preprocessed in -reconall in FS 5.3 using FS devel-20180612 using the command segmentHA_T1.sh bert [SUBJECTS_DIR]

This has worked successfully for roughly 1000 participants but for one participant I keep getting the error below, despite several troubleshooting attempts. The error appears to be "Fitting mesh to synthetic image resulted in no deformation" - I have had a look at the hippocampus masks and they appear to be registered incorrectly, despite the grey matter volume output from -reconall having no errors. Do you have any idea what I can do to troubleshoot or fix this?

Thanks in advance,

Sally.

Error Log:

------------------------------
USER valentil
HOST m3a007
PROCESSID 29440
PROCESSOR x86_64
OS Linux
Linux m3a007 3.10.0-514.26.1.el7.x86_64 #1 SMP Thu Jun 29 16:05:25 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
------------------------------

#--------------------------------------------
#@# Hippocampal Subfields processing (T1) left Tue Sep 25 10:18:56 AEST 2018
------------------------------------------
Setting up environment variables
---
LD_LIBRARY_PATH is .:/usr/local/freesurfer/devel-20180612/MCRv84//runtime/glnxa64:/usr/local/freesurfer/devel-20180612/MCRv84//bin/glnxa64:/usr/local/freesurfer/devel-20180612/MCRv84//sys/os/glnxa64:/usr/local/freesurfer/devel-20180612/lib/tcltktixblt:/usr/local/freesurfer/devel-20180612/lib/qt/lib:/usr/local/freesurfer/devel-20180612/lib/petsc/lib:/usr/local/freesurfer/devel-20180612/lib/KWWidgets/lib/KWWidgets:/usr/local/freesurfer/devel-20180612/lib/cuda/lib64:/usr/local/freesurfer/devel-20180612/lib/cuda/lib:/usr/local/freesurfer/devel-20180612/lib/bem:/usr/local/freesurfer/devel-20180612/lib:/usr/local/freesurfer/devel-20180612/lib/vtk/lib/vtk-5.6:/usr/local/fsl/5.0.11/fsl/lib:/usr/local/libjpeg-turbo/1.4.2/lib64:/usr/local/cuda/7.5/extras/CUPTI/lib64:/usr/local/cuda/7.5/lib64:/usr/local/cuda/7.5/lib:/usr/local/cuda/7.5/lib64/stubs:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm:/usr/local/tigervnc/1.8.0/lib64:/usr/local/tigervnc/1.8.0/lib:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm::/usr/local/freesurfer/devel-20180612/MCRv84//sys/opengl/lib/glnxa64:/usr/local/freesurfer/devel-20180612/lib/tcltktixblt:/usr/local/freesurfer/devel-20180612/lib/qt/lib:/usr/local/freesurfer/devel-20180612/lib/petsc/lib:/usr/local/freesurfer/devel-20180612/lib/KWWidgets/lib/KWWidgets:/usr/local/freesurfer/devel-20180612/lib/cuda/lib64:/usr/local/freesurfer/devel-20180612/lib/cuda/lib:/usr/local/freesurfer/devel-20180612/lib/bem:/usr/local/freesurfer/devel-20180612/lib:/usr/local/freesurfer/devel-20180612/lib/vtk/lib/vtk-5.6:/usr/local/fsl/5.0.11/fsl/lib:/usr/local/libjpeg-turbo/1.4.2/lib64:/usr/local/cuda/7.5/extras/CUPTI/lib64:/usr/local/cuda/7.5/lib64:/usr/local/cuda/7.5/lib:/usr/local/cuda/7.5/lib64/stubs:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm:/usr/local/tigervnc/1.8.0/lib64:/usr/local/tigervnc/1.8.0/lib:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm:
Registering imageDump.mgz to hippocampal mask from ASEG
$Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $

--mov: Using imageDump.mgz as movable/source volume.
--dst: Using /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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 )
MRIreorder() -----------
xdim=-1 ydim=3 zdim=-2
src 131 241 99, 0.250000 0.250000 0.250000
dst 131 99 241, 0.250000 0.250000 0.250000
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 (36, 31, 52)
   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 (36, 31, 61) voxels.
       -- Reslicing using cubic bspline
MRItoBSpline degree 3
    - reslicing Dst ...
       -- Original : (1, 1, 1)mm and (36, 31, 52) voxels.
       -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels.
       -- Reslicing using cubic bspline
MRItoBSpline degree 3

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: 0 )
   - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 )
   - initial transform:
Ti = [ ...
1.0000000000000                0                0 0.8149113483238
               0 1.0000000000000                0 0.6091444604181
               0                0 1.0000000000000 -10.4099368843102
               0                0                0 1.0000000000000 ]

   - initial iscale: Ii =1

Resolution: 0 S( 36 31 61 ) T( 36 31 61 )
Iteration(f): 1
     -- diff. to prev. transform: 8.43588
Iteration(f): 2
     -- diff. to prev. transform: 6.13157
Iteration(f): 3
     -- diff. to prev. transform: 5.20397
Iteration(f): 4
     -- diff. to prev. transform: 5.47621
Iteration(f): 5
     -- diff. to prev. transform: 8.03452 max it: 5 reached!

   - final transform:
Tf = [ ...
0.9476591624290 -0.1857780011274 0.2596702642998 -6.9596494990127
0.2461981066435 0.9430226622820 -0.2238185664945 2.3191313534194
-0.2032943780569 0.2760340426825 0.9394022584233 -11.1737673430040
               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 = [ ...
-0.2369147906072 0.0649175660749 0.0464445002818 20.3444784868448
-0.0615495266609 -0.0559546416236 -0.2357556655705 36.6877108257354
0.0508235945142 0.2348505646058 -0.0690085106706 -14.6334717181848
               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/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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

Highly Accurate Inverse Consistent Registration: A Robust ...

reuter.mit.edu

Highly Accurate Inverse Consistent Registration: A Robust Approach Martin Reutera,b,c,∗, H. Diana Rosasa,b, Bruce Fischla,b,c aMassachusetts General Hospital / Harvard Medical School, Boston, MA, USA bMartinos Center for Biomedical Imaging, 143 13th Street, Charlestown, MA, USA cMIT Computer Science and AI Lab, Cambridge, MA, USA Abstract ...

$Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $

--mov: Using imageDump.mgz as movable/source volume.
--dst: Using /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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: Enableing affine transform!
--sat: Using saturation 50 in M-estimator!

reading source 'imageDump.mgz'...
reading target '/home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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 )
MRIreorder() -----------
xdim=-1 ydim=3 zdim=-2
src 131 241 99, 0.250000 0.250000 0.250000
dst 131 99 241, 0.250000 0.250000 0.250000
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 (36, 31, 52)
   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 (36, 31, 61) voxels.
       -- Reslicing using cubic bspline
MRItoBSpline degree 3
    - reslicing Dst ...
       -- Original : (1, 1, 1)mm and (36, 31, 52) voxels.
       -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels.
       -- Reslicing using cubic bspline
MRItoBSpline degree 3

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: 0 )
   - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 )
   - initial transform:
Ti = [ ...
1.0000000000000                0                0 0.8149148880414
               0 1.0000000000000                0 0.6090566088288
               0                0 1.0000000000000 -10.4098892270167
               0                0                0 1.0000000000000 ]

   - initial iscale: Ii =1

Resolution: 0 S( 36 31 61 ) T( 36 31 61 )
Iteration(f): 1
     -- diff. to prev. transform: 41.9486
Iteration(f): 2
     -- diff. to prev. transform: 11.6817
Iteration(f): 3
     -- diff. to prev. transform: 6.67838
Iteration(f): 4
     -- diff. to prev. transform: 7.29474
Iteration(f): 5
     -- diff. to prev. transform: 6.81314 max it: 5 reached!

   - final transform:
Tf = [ ...
0.7187784317010 -0.7039049689666 0.2089453505114 9.6512053486057
0.0746036190898 0.1293914974162 0.0117417560783 14.1524707759350
-0.2465560963278 -0.6917283813412 1.0760848577522 2.1884563417970
               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 = [ ...
-0.1796946067577 0.0522363297057 0.1759762437984 14.8124989197474
-0.0186509047068 0.0029354397057 -0.0323478742666 20.2771618184274
0.0616390300951 0.2690212102028 0.1729321033528 -29.4307376768930
               0                0                0 1.0000000000000 ]

Determinant : -0.00250544

Decompose into Rot * Shear * Scale :

Rot = [ ...
-0.8007108168802 0.0158873366257 0.5988403629232
-0.5106339926748 0.5045973184270 -0.6961569304125
0.3132333207947 0.8632086299216 0.3959239168991 ]

Shear = [ ...
1.0000000000000 0.1745654451980 -0.3575927026144
0.2370458412539 1.0000000000000 0.6912992608189
-0.4065670242970 0.5788093472423 1.0000000000000 ]

Scale = diag([ 0.1727145993906 0.2345325414370 0.1963688302739 ])

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/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/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

Highly Accurate Inverse Consistent Registration: A Robust ...

reuter.mit.edu

Reading contexts of file /usr/local/freesurfer/devel-20180612/average/HippoSF/atlas/compressionLookupTable.txt
Constructing image-to-world transform from header information (asegModCHA.mgz)
Constructing image-to-world transform from header information (/home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left/imageDump.mgz)
Transforming points
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Reading contexts of file /usr/local/freesurfer/devel-20180612/average/HippoSF/atlas/compressionLookupTable.txt
--------------
Making Left-Cerebral-Cortex map to reduced label 1
Making alveus map to reduced label 1
Making subiculum-body map to reduced label 1
Making subiculum-head map to reduced label 1
Making Hippocampal_tail map to reduced label 1
Making molecular_layer_HP-body map to reduced label 1
Making molecular_layer_HP-head map to reduced label 1
Making GC-ML-DG-body map to reduced label 1
Making GC-ML-DG-head map to reduced label 1
Making CA4-body map to reduced label 1
Making CA4-head map to reduced label 1
Making CA1-body map to reduced label 1
Making CA1-head map to reduced label 1
Making CA3-body map to reduced label 1
Making CA3-head map to reduced label 1
Making HATA map to reduced label 1
Making fimbria map to reduced label 1
Making presubiculum-body map to reduced label 1
Making presubiculum-head map to reduced label 1
Making parasubiculum map to reduced label 1
Making Lateral-nucleus map to reduced label 1
Making Paralaminar-nucleus map to reduced label 1
Making Basal-nucleus map to reduced label 1
Making Accessory-Basal-nucleus map to reduced label 1
Making Corticoamygdaloid-transitio map to reduced label 1
Making Central-nucleus map to reduced label 1
Making Cortical-nucleus map to reduced label 1
Making Medial-nucleus map to reduced label 1
Making Anterior-amygdaloid-area-AAA map to reduced label 1
--------------
Making Left-Cerebral-White-Matter map to reduced label 2
--------------
Making Left-Lateral-Ventricle map to reduced label 3
--------------
Making Left-choroid-plexus map to reduced label 4
--------------
Making hippocampal-fissure map to reduced label 5
Making Unknown map to reduced label 5
--------------
Making Left-VentralDC map to reduced label 6
--------------
Making Left-Putamen map to reduced label 7
--------------
Making Left-Pallidum map to reduced label 8
--------------
Making Left-Thalamus-Proper map to reduced label 9
--------------
Making Left-Accumbens-area map to reduced label 10
--------------
Making Left-Caudate map to reduced label 11
Smoothing mesh collection with kernel size 3.000000 ...Smoothing class: 0
Smoothing class: 1
Smoothing class: 2
Smoothing class: 3
Smoothing class: 4
Smoothing class: 5
Smoothing class: 6
Smoothing class: 7
Smoothing class: 8
Smoothing class: 9
Smoothing class: 10
done
numberOfContrasts = 1
Image: 4
AtlasMeshToIntensityImage
1
0.398942
SLIDING
L-BFGS
Verbose: 0
MaximalDeformationStopCriterion: 1e-10
LineSearchMaximalDeformationIntervalStopCriterion: 1e-10
MaximumNumberOfIterations: 1000
BFGS-MaximumMemoryLength: 12
Resolution 1, iteration 1
Did one deformation step of max. 0 voxels in 4.2457 seconds

minLogLikelihoodTimesPrior =

2.4171e-16

Smoothing mesh collection with kernel size 2.000000 ...Smoothing class: 0
Smoothing class: 1
Smoothing class: 2
Smoothing class: 3
Smoothing class: 4
Smoothing class: 5
Smoothing class: 6
Smoothing class: 7
Smoothing class: 8
Smoothing class: 9
Smoothing class: 10
done
numberOfContrasts = 1
Image: 4
AtlasMeshToIntensityImage
1
0.398942
SLIDING
L-BFGS
Verbose: 0
MaximalDeformationStopCriterion: 1e-10
LineSearchMaximalDeformationIntervalStopCriterion: 1e-10
MaximumNumberOfIterations: 1000
BFGS-MaximumMemoryLength: 12
Resolution 2, iteration 1
Did one deformation step of max. 0 voxels in 4.2086 seconds

minLogLikelihoodTimesPrior =

2.4171e-16

Fitting mesh to synthetic image from ASEG took 21.6669 seconds
Error using segmentSubjectT1_autoEstimateAlveusML (line 533)
Fitting mesh to synthetic image resulted in no deformation

@#@FSTIME 2018:09:25:10:18:56 run_segmentSubjectT1_autoEstimateAlveusML.sh N 13 e 65.81 S 1.73 U 34.89 P 55% M 1012356 F 271 R 219755 W 0 c 3155 w 11451 I 146312 O 477024 L 11.09 10.51 11.31
@#@FSLOADPOST 2018:09:25:10:20:02 run_segmentSubjectT1_autoEstimateAlveusML.sh N 13 35.39 16.50 13.25
Linux m3a007 3.10.0-514.26.1.el7.x86_64 #1 SMP Thu Jun 29 16:05:25 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux

T1 hippocampal subfields exited with ERRORS at Tue Sep 25 10:20:02 AEST 2018

--

Sally Grace, BSc (Hons), PhD Candidate.
Centre for Mental Health, Faculty of Health, Arts and Design

Swinburne University of Technology