Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 21

14 Oct 2019

      External Email - Use Caution
Dear Juan Eugenio, thanks for your answer. I tried to do with the corrected
images and wit the raw ones (before the correction), and I got the same
error. Then I tried with two patients more and happened the same. I don´t
know if it is due to the dev version we used, because the first time it
worked very well.
Best regards,
JC.
El dom., 13 oct. 2019 a las 12:01, freesurfer-request@nmr.mgh.harvard.edu
escribió:
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Today's Topics:

HippoAmyg (Juan Rivas)
Learning dti processing tutorial (Renew Andrade)
Re: HippoAmyg (Iglesias Gonzalez, Juan E.)

Message: 1
Date: Sat, 12 Oct 2019 16:02:28 -0400
From: Juan Rivas jcrn12@gmail.com
Subject: [Freesurfer] HippoAmyg
To: freesurfer@nmr.mgh.harvard.edu
Message-ID:
        <CACYE61CZSKt0p-q=
k+Yph1dDvpW4O67qehmXfhVpem7gqobdUg@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
    External Email - Use Caution

*Hi, I runned the reconall of my images with FS60 with this command:*
*shiraz[0]:NIFTI$ recon-all -i
/autofs/cluster/neuromod/rivas/imagenes/NIFTI/sub-esq-02-en/anat/sub-esq-02-en_T1w.nii.gz
-s /autofs/cluster/neuromod/rivas/subject-esq-02-en. There were no errors.*
*Then I runned recon for hippocampus and amygdala with fsdev on Thu Aug 22
15:36:32 , with this command:*
*segmentHA_T1.sh*
*There were no errors. Then I identified and corrected manually the errors
on the FS60 images.*
*Then I run recon-all on fs60 without to touch hippo-amyg.*
*Now, I am trying to make the hippo-amyg correction with this command:*
*segmentHA_T1.sh on fsdev, and I got this error:*
[shiraz:FS] (nmr-dev-env) segmentHA_T1.sh test1
#--------------------------------------------
#@# Hippocampal Subfields processing (T1) left Fri Oct 11 17:20:27 EDT 2019
/usr/bin/time -o /dev/stdout
@#@FSTIME 2019:10:11:17:20:27 run_segmentSubjectT1_autoEstimateAlveusML.sh
N 13 e %e S %S U %U P %P M %M F %F R %R W %W c %c w %w I %I O %O L 1.23
1.35 1.67
run_segmentSubjectT1_autoEstimateAlveusML.sh
/usr/local/freesurfer/dev/MCRv84/ test1 /cluster/neuromod/rivas/imagenes/FS
0.333333333333333333333333333333333333
/usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasMesh.gz
/usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasDump.mgz
/usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt
0.05 left L-BFGS v21 /usr/local/freesurfer/dev/bin/ 0

Setting up environment variables

LD_LIBRARY_PATH is
.:/lib64:/usr/local/freesurfer/dev/MCRv84//runtime/glnxa64:/usr/local/freesurfer/dev/MCRv84//bin/glnxa64:/usr/local/freesurfer/dev/MCRv84//sys/os/glnxa64:/native_threads:/server:/client::
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
/cluster/neuromod/rivas/imagenes/FS/test1/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
'/cluster/neuromod/rivas/imagenes/FS/test1/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 (37, 33, 61)
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 (37, 33, 61) 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: 0 )

Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 )

initial transform:

Ti = [ ...
1.0000000000000                0                0 -0.9335110261151
           0  1.0000000000000                0 -0.6030053897425

           0                0  1.0000000000000 -1.9033167008449

           0                0                0  1.0000000000000  ]

initial iscale:  Ii =1

Resolution: 0  S( 37 33 61 )  T( 37 33 61 )
Iteration(f): 1
 -- diff. to prev. transform: 17.9258

Iteration(f): 2
 -- diff. to prev. transform: 13.3727

Iteration(f): 3
 -- diff. to prev. transform: 12.6349

Iteration(f): 4
 -- diff. to prev. transform: 0.963353

Iteration(f): 5
 -- diff. to prev. transform: 0.23376 max it: 5 reached!

final transform:

Tf = [ ...
0.9994502743718 -0.0309610775894 -0.0118558311665  0.1083605389283
0.0330356012422  0.9601637341023  0.2774783825190 -11.0026122646332
0.0027925093931 -0.2777175100517  0.9606587253036  3.8161835807274
           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 = [ ...
-0.2498625685929 -0.0029639577916  0.0077402693973 33.8236195063683
-0.0082589003105  0.0693695956298 -0.2400409335256 17.7299867104994
-0.0006981273483  0.2401646813259  0.0694293775129 -3.6765424847757
           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
/cluster/neuromod/rivas/imagenes/FS/test1/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
$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
/cluster/neuromod/rivas/imagenes/FS/test1/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
'/cluster/neuromod/rivas/imagenes/FS/test1/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 (37, 33, 61)
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 (37, 33, 61) 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: 0 )

Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 )

initial transform:

Ti = [ ...
1.0000000000000                0                0 -0.9335121201217
           0  1.0000000000000                0 -0.6030049400697

           0                0  1.0000000000000 -1.9033196349668

           0                0                0  1.0000000000000  ]

initial iscale:  Ii =1

Resolution: 0  S( 37 33 61 )  T( 37 33 61 )
Iteration(f): 1
 -- diff. to prev. transform: 29.7552

Iteration(f): 2
 -- diff. to prev. transform: 13.9258

Iteration(f): 3
 -- diff. to prev. transform: 12.8176

Iteration(f): 4
 -- diff. to prev. transform: 4.31284

Iteration(f): 5
 -- diff. to prev. transform: 1.08934 max it: 5 reached!

final transform:

Tf = [ ...
1.1485282870250  0.1923732018210  0.0456719546813 -8.8430815689836
0.0248585691740  1.1974718877709  0.2901612074595 -15.4946754855698
0.0132871026014  0.0262311630292  0.9721734242629 -1.7628900186542
           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 = [ ...
-0.2871321056267  0.0114179894966 -0.0480933061884 36.4485194686672
-0.0062146408039  0.0725403105008 -0.2993680076302 19.7524929053736
-0.0033217759975  0.2430433850326 -0.0065577915391 -0.1873902167255
           0                0                0  1.0000000000000  ]

Determinant : -0.020683
Decompose into Rot * Shear * Scale :
Rot = [ ...
-0.9973893744923  0.0079822957206 -0.0717685070543
0.0722067536928  0.1210866300984 -0.9900122285773
-0.0007876362909  0.9926098483122  0.1213468939142  ]
Shear = [ ...
1.0000000000000 -0.0253544642652  0.0881389503515
-0.0221787471386  1.0000000000000 -0.1442736100723
0.0921761860222 -0.1724865310828  1.0000000000000  ]
Scale = diag([  0.2859363885412  0.2501220610640  0.2990338055489  ])
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
/cluster/neuromod/rivas/imagenes/FS/test1/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
Reading contexts of file
/usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt
Constructing image-to-world transform from header information
(asegModCHA.mgz)
Constructing image-to-world transform from header information
(/cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left/imageDump.mgz)
Transforming points
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Reading contexts of file
/usr/local/freesurfer/dev/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-Accumbens-area map to reduced label 9

Making Left-Caudate map to reduced label 10
Error using segmentSubjectT1_autoEstimateAlveusML (line 365)
The vector of prior probabilities in the mesh nodes must always sum to one
over all classes.
How may I correct it?
Thanks  lot,
JC.

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Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 21