Freesurfer August 2020

freesurfer@nmr.mgh.harvard.edu

101 participants
152 discussions

How can I get only aparc+aseg.mgz
by Camargo, Aldo 28 Aug '20

28 Aug '20

External Email - Use Caution Hi Freesurfer experts: I would like to get the aparc+aseg.mgz file, which options of recon-all should I use to get only that file. Because recon-all takes several hours. Have a nice day, Aldo

2 1

Question about LGI stats file
by Lab of Autism and Developmental Neuroscience, Lab of Autism and Developmental Neuroscience 28 Aug '20

28 Aug '20

External Email - Use Caution Dear FreeSurfer experts, Hope this email finds you well. I was wondering if someone could please help clarify some components of the LGI pipeline. So the wiki tells us the following command needs to be done to obtain a stats file with the LGI info for one subject: mri_segstats --annot my_subject_id lh aparc --i $SUBJECTS_DIR/my_subject_id/surf/lh.pial_lgi --sum lh.aparc.pial_lgi.stats Should I perform this for each subject and then join them all into one file? I was hoping that you guys could direct me to some stats2table command that could do all of this automatically and thus be less time consuming. Thank you and stay safe All the best, Alex

2 2

Covariates vs sample size
by Sunil Khokhar 28 Aug '20

28 Aug '20

External Email - Use Caution Dear Experts, With a sample size of 45, can I perform GLM analysis with one primary independent variable and 6 covariates? Dependent variable is cortical thickness. Thanks in advance! Sunil.

2 1

missing one tract but differently for a few subjects
by Zeng, Qi 27 Aug '20

27 Aug '20

External Email - Use Caution Hi, When I examined the 18 tracts of /dpath results, a few subjects were found missing one tract but differently, for example, fmajor were missing for one subject and the other one was missing lh.slfp and rh.slfp. I was thinking could it be that AP-PA was flipped. Attached screenshots of dtifit_FA.nii, aparc+seg_mask.nii, and bvec. Can you provide some information about the flip so that I can examine it for the rest of the subjects? Best, Qi

2 3

Visualizing significant clusters, monte-carlo and permutation
by Avi Gharehgazlou 27 Aug '20

27 Aug '20

External Email - Use Caution Hi experts, I am a bit confused with visualizing my significant clusters after having corrected my results for multiple comparisons. after running mri_glmfit, I corrected with monte carlo and permutation as explained below, will you please confirm my understanding below is correct ? 1. I ran monte carlo simulation (1.3 sig level). My understanding is that "cache.th13.abs.sig.cluster.summary" shows the clusters that are significant after correction for multiple comparisons. I have 1 cluster listed in this file. Thus, I am interpreting this as having 1 significant cluster after correction at p<0.05, is this correct? When I want to visualize my corrected results I use the command line below. I am assuming this command line displays only the significant clusters that survived multiple comparison correction because only the cluster that is listed in my "cache.th13.abs.sig.cluster.summary" file appears on the brain here, and because I have put " cache.th13.abs.sig.cluster.mgh" and as overlay " cache.th13.abs.sig.ocn.annot" and set the threshold to 1.3 in my command line. Is it correct that the cluster that appears when running the below command line is my significant cluster surviving multiple comparisons correction at p<0.05? vglrun freeview -f $SUBJECTS_DIR/fsaverage/surf/lh.inflated :overlay=lh.lgi.TD_PTSD_age_SA_IQ_FWHM0.glmdir/group.diff/cache.th13.abs.sig.cluster.mgh:overlay_ threshold=1.3,5:annot=lh.lgi.TD_PTSD_age_SA_IQ_FWHM0.glmdir/group.diff/cache.th13.abs.sig.ocn.annot -viewport 3d also, how can I get the clusters to look their appropriate colours when I am vieweing them with the command line above? all my clusters appear gray (regardless of whether they have positive or negative max values, in which case they should be yellow and blue, respectively) 2. I then ran permutation testing. My "perm.th13.abs.sig.cluster.summary" file is blank. I interpret this as no clusters having survived multiple comparison correction with permutation testing. Am I correct? When I use the below command line to visualize my result I see a blank brain (with no clusters coloured). Which makes sense to me because my "perm.th13.abs.sig.cluster.summary" also showed no significant clusters. is this interpretation and the command line I am using below correct? vglrun freeview -f $SUBJECTS_DIR/fsaverage/surf/lh.inflated:overlay=lh.lgi.TD_PTSD_age_SA_IQ_FWHM0.glmdir/group.diff/perm.th13.abs.sig.cluster.mgh:overlay_threshold=1.3,5:annot=lh.lgi.TD_PTSD_age_SA_IQ_FWHM0.glmdir/group.diff/perm.th13.abs.sig.ocn.annot -viewport 3d -layout 1 I think my understanding is correct, but I am confused because the FreeSurfer instructions state that the above command lines will display "all clusters, regardless of significance"... but using the above command lines seems to only display my significant clusters. I am assuming this is because I set my threshold to 1.3 (the same threshold I used to correct for multiple comparisons in 1 and 2 above). But wanted to make sure I am correct before proceeding. Thank you so much for your support, Avi

2 7

FW: RBV PVC
by Nathalie Mertens 27 Aug '20

27 Aug '20

External Email - Use Caution Dear, Many thanks for these explanations! Further, I do have 4 additional questions linked to the mask option when applying RBV PVC in Freesurfer. (1) Could you please tell me how the brainmask is generated when using the –auto-mask option, eg –-auto-mask 5 0.01. From which image is the generation of the mask started? (brain.mgz, brainmask.auto.mgz, brainmask.mgz, or something else?) (2) I noticed that the amount of segments (used in RBV PVC) is reduced (compared to amount of roi’s in gtmseg) after using the mask. Can you explain this? (3) Could you please tell me when in the RBV PVC calculations this mask is applied? (4) I guess this mask has nothing to do with the ‘masking rbv to brain’, which is applied in the end when running the RBV PVC? Can you elaborate on this final step? Many thanks for your help. Kind regards, Nathalie Mertens On 8/19/2020 2:42 AM, Nathalie Mertens wrote: External Email - Use Caution Dear, I am not sure if the questions below were sent correctly to you, since I did not receive any copy of it. Please find my message below. Many thanks, looking forward to your reply. Kind regards, Nathalie Mertens sorry, did not see the original posting *Van: *Nathalie Mertens <nathalie.mert...(a)kuleuven.be> *Datum: *dinsdag 18 augustus 2020 om 09:33 *Aan: *"freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> *Onderwerp: *RBV PVC Dear, I do have some questions linked to RBV PVC implemented in Freesurfer: 1. Could you please tell me how the RBV PVC is acquired? Is the RBV PVC acquired based on Muller-Gartner (on a region-level, and based on only using gray- versus white matter), or when and how are the different regions from gtm_seg applied to obtain GTM? See the RBV paper. Thomas, B.A., Erlandsson, K., Modat, M., Thurfjell, L., Vandenberghe, R., Ourselin, S., Hutton, B.F., 2011. The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. Eur. J. Nucl. Med. Mol. Imaging 38, 1104\u20131119. 1. 2. What would be the advantage of using your technique compared to for instance sGTM? sGTM is ROI-only, RBV is voxelwise. 1. 2. Could you please tell me the difference in region-definition between aparc+aseg and the gtm_seg? (I saw that the latter contains less regions compared to aparc+aseg.) What is the reason to use the latter compared to the aparc+aseg, based on the definitions of the regions? (I know that gtm_seg is used which has a high resolution, but not quite sure, apart from the resolution, why you are not using aparc+aseg for the RBV PVC.) gtmseg.mgz is higher resolution (using the subvoxel surfaces). It also has pons and vermis defined. It has a few regions merged with others. It also has extracerebral structures. 1. 2. Is there a mask applied to the gtm_seg to exclude regions that are located outside the brain? No, it is a full head model. 1. Many thanks for your answers. Kind regards, Nathalie _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

2 1

Segmentation of CT Scans
by mudassar liaq 27 Aug '20

27 Aug '20

External Email - Use Caution Hi, As far as I searched, Freesurfer does not segment CT Scans. Can anybody guide me to how I can segment Brain CT Scans in an automated way. (Any pointer or guiders are welcome). I have searched but couldn't find something similar to freesurfer (MRI). Thanks and Regards, Liaq.

2 1

How to only include frontal and temporal lobes in surface based group analyses and multiple comparison correction
by Paul 27 Aug '20

27 Aug '20

2 1

Freesurfer hippocampal error
by Emily K Clarke 27 Aug '20

27 Aug '20

External Email - Use Caution Hi all, I wanted to follow up on Courtney Haswell’s previous email – we’re still running into the error where the hippocampal files aren’t being created when we run our scans through the recon_all using the -hippocampal-subfields flag. I’ve included the hippocampal log file below. Do you have any insight on why this might be happening? Thanks, Emily Clarke-Rubright The whole log message: #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) left Mon Aug 10 18:54:21 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 (38, 40, 59) 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, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459029727155 0 1.0000000000000 0 -0.0876360601093 0 0 1.0000000000000 -2.9034875996918 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.11234 Iteration(f): 2 -- diff. to prev. transform: 5.92976 Iteration(f): 3 -- diff. to prev. transform: 7.00993 Iteration(f): 4 -- diff. to prev. transform: 4.91305 Iteration(f): 5 -- diff. to prev. transform: 0.781018 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 8.1296 Iteration(f): 2 -- diff. to prev. transform: 1.32542 Iteration(f): 3 -- diff. to prev. transform: 0.660013 Iteration(f): 4 -- diff. to prev. transform: 0.272219 Iteration(f): 5 -- diff. to prev. transform: 0.0943126 max it: 5 reached! - final transform: Tf = [ ... 0.9986737402682 -0.0371977207162 0.0355962086772 0.4749903609000 0.0194355513105 0.9126027300469 0.4083852549446 -14.0571487737101 -0.0476761978762 -0.4071517980857 0.9121153400046 9.9144950322154 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 = [ ... -0.2496684350671 0.0088990521693 0.0092994301791 34.3717771918614 -0.0048588878276 0.1020963137361 -0.2281506825117 16.0960341057205 0.0119190494690 0.2280288350011 0.1017879495214 -5.4978401025085 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 (38, 40, 59) 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, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459028059189 0 1.0000000000000 0 -0.0876339827813 0 0 1.0000000000000 -2.9034861096682 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.39891 Iteration(f): 2 -- diff. to prev. transform: 6.07128 Iteration(f): 3 -- diff. to prev. transform: 6.45354 Iteration(f): 4 -- diff. to prev. transform: 9.61165 Iteration(f): 5 -- diff. to prev. transform: 6.45157 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 18.1397 Iteration(f): 2 -- diff. to prev. transform: 10.4781 Iteration(f): 3 -- diff. to prev. transform: 4.71708 Iteration(f): 4 -- diff. to prev. transform: 2.16703 Iteration(f): 5 -- diff. to prev. transform: 0.732237 max it: 5 reached! - final transform: Tf = [ ... 1.1003251237945 0.4596695415609 0.1319823678432 -15.3375075204562 -0.0809129505733 1.0958357713115 0.4294815397408 -16.7169936140805 -0.0871431123400 -0.3796938017010 0.9044729973197 10.3182069614882 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 = [ ... -0.2750812479116 0.0329955927294 -0.1149173912565 38.1277797083535 0.0202282358422 0.1073703848787 -0.2739589559572 15.8057165631404 0.0217857755217 0.2261182492690 0.0949234628220 -5.6011779490725 0 0 0 1.0000000000000 ] Determinant : -0.0203612 Decompose into Rot * Shear * Scale : Rot = [ ... -0.9711353975406 0.1063152910440 -0.2135254048909 0.2376286779964 0.3535137619413 -0.9047434064484 0.0207038894236 0.9293681073764 0.3685732898005 ] Shear = [ ... 1.0000000000000 -0.0073421724799 0.1576683162397 -0.0067818833526 1.0000000000000 -0.0678198022274 0.1779196232492 -0.0828533754661 1.0000000000000 ] Scale = diag([ 0.2723989962754 0.2516119340902 0.3073865945084 ]) 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: 0.275081 -0.0329956 0.114917 0.0217858 0.226118 0.0949235 -0.0202282 -0.10737 0.273959 Offset: [-38.0874, -33.402, 1.94475] Center: [0, 0, 0] Translation: [-38.0874, -33.402, 1.94475] Inverse: 3.54297 -0.162038 -1.43002 -0.38743 3.81537 -1.15946 0.109759 1.48336 3.09018 Singular: 0 Transform before cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [170.128, 142.806, 78.3988] Center: [0, 0, 0] Translation: [170.128, 142.806, 78.3988] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [123.105, 115.958,78.3988] maximalMappedCoordinate: [178.047, 171.204,144.802] Cropping with min [118 110 72] and max [184 177 151] m_OriginalImageRegion before checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion before checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] m_OriginalImageRegion after checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion after checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] Transform after cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [52.1278, 32.8057, 6.39882] Center: [0, 0, 0] Translation: [52.1278, 32.8057, 6.39882] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [14.0404, 34.7505, 39.8009] Center: [0, 0, 0] Translation: [14.0404, 34.7505, 39.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Careful here: the applied transformation will turn positive tetrahedra into negative ones. -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 determinant: -0.0203612 Starting to swap the point assignments of each tetrahedron... Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt -------------- 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 alveus map to reduced label 2 Making Hippocampal_tail map to reduced label 2 Making HATA map to reduced label 2 Making fimbria map to reduced label 2 Making parasubiculum map to reduced label 2 Making hippocampal-fissure map to reduced label 2 -------------- Making Left-Cerebral-Cortex map to reduced label 3 -------------- Making Left-Cerebral-White-Matter map to reduced label 4 -------------- Making Left-Lateral-Ventricle map to reduced label 5 -------------- Making Left-choroid-plexus map to reduced label 6 -------------- Making Unknown map to reduced label 7 -------------- Making Left-VentralDC map to reduced label 8 -------------- Making Left-Putamen map to reduced label 9 -------------- Making Left-Pallidum map to reduced label 10 -------------- Making Left-Thalamus-Proper map to reduced label 11 -------------- Making Left-Accumbens-area map to reduced label 12 -------------- Making Left-Caudate map to reduced label 13 Error using segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) right Mon Aug 10 18:54:37 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 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 (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781192630761 0 1.0000000000000 0 1.0539982530706 0 0 1.0000000000000 -3.1974090629449 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 13.2959 Iteration(f): 2 -- diff. to prev. transform: 9.20966 Iteration(f): 3 -- diff. to prev. transform: 6.22883 Iteration(f): 4 -- diff. to prev. transform: 8.12577 Iteration(f): 5 -- diff. to prev. transform: 6.11784 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 5.83495 Iteration(f): 2 -- diff. to prev. transform: 1.34307 Iteration(f): 3 -- diff. to prev. transform: 0.589867 Iteration(f): 4 -- diff. to prev. transform: 0.289573 Iteration(f): 5 -- diff. to prev. transform: 0.137676 max it: 5 reached! - final transform: Tf = [ ... 0.9980802661280 -0.0059694736544 0.0616453384301 -1.7131111872539 -0.0239991870640 0.8803011391816 0.4738079182283 -13.5066607583463 -0.0570948455303 -0.4743777711274 0.8784679327523 13.0909553444701 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 = [ ... 0.2495200665320 0.0154113346075 0.0014923684136 0.7292410424572 -0.0059997967660 0.1184519795571 -0.2200752847954 16.2081470043947 -0.0142737113826 0.2196169831881 0.1185944427819 -4.6804480513511 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 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 (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781175809123 0 1.0000000000000 0 1.0539986543476 0 0 1.0000000000000 -3.1974083471847 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 9.84742 Iteration(f): 2 -- diff. to prev. transform: 7.72517 Iteration(f): 3 -- diff. to prev. transform: 8.94618 Iteration(f): 4 -- diff. to prev. transform: 7.30294 Iteration(f): 5 -- diff. to prev. transform: 5.69484 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 11.7013 Iteration(f): 2 -- diff. to prev. transform: 3.26787 Iteration(f): 3 -- diff. to prev. transform: 1.06278 Iteration(f): 4 -- diff. to prev. transform: 0.347277 Iteration(f): 5 -- diff. to prev. transform: 0.0826464 max it: 5 reached! - final transform: Tf = [ ... 1.1636664104160 -0.3014738824441 0.0004403516595 4.0520187720971 -0.0480158583329 1.0596278475093 0.4954259490783 -17.9010148715481 -0.2453688431946 -0.4779665918288 0.8802303084621 16.7816318160186 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 = [ ... 0.2909164812245 0.0001100842157 0.0753684565922 -3.0302464159984 -0.0120039592287 0.1238564808785 -0.2649069144859 17.7887142169889 -0.0613421845898 0.2200575304725 0.1194916267047 -1.6040021052477 0 0 0 1.0000000000000 ] Determinant : 0.0216399 Decompose into Rot * Shear * Scale : Rot = [ ... 0.9808313831675 0.1119800663090 0.1594686882846 0.0897438323260 0.4668219029177 -0.8797859714247 -0.1729619679387 0.8772330224665 0.4478240524369 ] Shear = [ ... 1.0000000000000 -0.1069794540990 0.0987385001868 -0.0910164402411 1.0000000000000 -0.0348388469911 0.0999837864599 -0.0414655431721 1.0000000000000 ] Scale = diag([ 0.2948725983257 0.2508729779031 0.2985915205121 ]) 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: -0.290916 -0.000110086 -0.0753684 -0.0613422 0.220058 0.119492 0.012004 -0.123856 0.264907 Offset: [49.0706, -22.4049, 0.96176] Center: [0, 0, 0] Translation: [49.0706, -22.4049, 0.96176] Inverse: -3.37777 -0.432721 -0.765819 -0.81721 3.51948 -1.82003 -0.229025 1.66513 2.95866 Singular: 0 Transform before cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [82.9698, 143.789, 89.396] Center: [0, 0, 0] Translation: [82.9698, 143.789, 89.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [82.9698, 116.267,81.4215] maximalMappedCoordinate: [128.201, 173.514,153.92] Cropping with min [78 111 74] and max [133 179 161] m_OriginalImageRegion before checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion before checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] m_OriginalImageRegion after checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion after checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] Transform after cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [4.96976, 32.7887, 15.396] Center: [0, 0, 0] Translation: [4.96976, 32.7887, 15.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [54.0404, 33.7505, 37.8009] Center: [0, 0, 0] Translation: [54.0404, 33.7505, 37.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt -------------- 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 alveus map to reduced label 2 Making Hippocampal_tail map to reduced label 2 Making HATA map to reduced label 2 Making fimbria map to reduced label 2 Making parasubiculum map to reduced label 2 Making hippocampal-fissure map to reduced label 2 -------------- Making Left-Cerebral-Cortex map to reduced label 3 -------------- Making Left-Cerebral-White-Matter map to reduced label 4 -------------- Making Left-Lateral-Ventricle map to reduced label 5 -------------- Making Left-choroid-plexus map to reduced label 6 -------------- Making Unknown map to reduced label 7 -------------- Making Left-VentralDC map to reduced label 8 -------------- Making Left-Putamen map to reduced label 9 -------------- Making Left-Pallidum map to reduced label 10 -------------- Making Left-Thalamus-Proper map to reduced label 11 -------------- Making Left-Accumbens-area map to reduced label 12 -------------- Making Left-Caudate map to reduced label 13 Error using segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes

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Re: [Freesurfer] Error using segmentHA_T1.sh
by Iglesias Gonzalez, Juan E. 27 Aug '20

27 Aug '20

Hi Lou, Can you please send us your exact command line? Cheers, /Eugenio Juan Eugenio Iglesias Senior research fellow CMIC (UCL), MGH (HMS) and CSAIL (MIT) http://www.jeiglesias.com From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of "Blanpain, Lou" <lou.blanpain(a)emory.edu> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Wednesday, August 26, 2020 at 20:56 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Error using segmentHA_T1.sh External Email - Use Caution Hello, I attempted using segmentHA_T1.sh after running recon-all on a subject, but got the following error: “ Error using segmentSubjectT1_autoEstimateAlveusML (line 72) Side must be 'left' or 'right' “ I am using the following specs: _ platform: MacOS Catalina version 10.15.5 _ freesurfer version: 7.1.0 Could you help me with this matter? Thank you so much in advance for any input you may be able to provide regarding this! ________________________________ This e-mail message (including any attachments) is for the sole use of the intended recipient(s) and may contain confidential and privileged information. If the reader of this message is not the intended recipient, you are hereby notified that any dissemination, distribution or copying of this message (including any attachments) is strictly prohibited. If you have received this message in error, please contact the sender by reply e-mail message and destroy all copies of the original message (including attachments).

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Freesurfer August 2020