Freesurfer

freesurfer@nmr.mgh.harvard.edu

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30084 discussions

ERROR: fast_selxavg3() failed\n
by mahdiyeh khanbagi 02 Mar '20

02 Mar '20

External Email - Use Caution Dear FreeSurfer Developers, I am trying to do FSFAST analysis on Mac OS Catalina 10.15.3, and I have completed the preprocessing (preproc-sess, mkanalysis-sess and mkcontrast-sess) with no problem. When I ran the function selxavg3-sess, it proceeded somehow (-->preproc-sess done--> MATLAB opened and then was closed), but when it started running fast_selxavg3b it stopped after a while (fast_selxavg3() failed\n) with the following errors: Extension format = nii.gz INFO: key nSliceGroups unrecognized, line 12, skipping 1 AnimalvsNonAnimal.mat Excluding 4 points Subscript indices must either be real positive integers or logicals. Error in fast_st2fir (line 118) Xfirss(ind,nthd) = wstim; Error in flac_desmat (line 58) flacnew.ev(nthev).Xfir = fast_st2fir(st,ntpssr,TER,ev.psdwin,useweight); Error in flac_customize (line 357) flacnew = flac_desmat(flacnew); Error in fast_selxavg3b (line 129) flac0 = flac_customize(flac0); Could anyone help me with this problem, please? Best Regards, Mahdiyeh

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make_average_subject
by Benjamin Ineichen 02 Mar '20

02 Mar '20

External Email - Use Caution Dear Freesurfer experts, I want to create a new standard template from my study population. For this, I thought about using make_average_subject. Thus, I run: make_average_subject --out avgsubject --subjects REMY001 REMY002 REMY003 I get an error message - ERROR: make_average_surface. Please find attached the error.log. Would be very thankful for advice! Many thanks! /Ben -- ____________________________________ Benjamin Victor Ineichen, MD PhD Karolinska Institutet Center for Molecular Medicine Stockholm, Sweden ineichen(a)protonmail.ch +41 76 391 04 01 Dr. med. et Dr. rer. nat. ETH Benjamin Victor Ineichen Karolinska Institutet Center for Molecular Medicine Stockholm, Sweden ineichen(a)protonmail.ch +41 76 391 04 01

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Freesurfer Digest, Vol 193, Issue 3
by Faith Gunning 02 Mar '20

02 Mar '20

External Email - Use Caution Thanks, Lauren. Faith ________________________________ From: freesurfer-bounces(a)nmr.mgh.harvard.edu <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of freesurfer-request(a)nmr.mgh.harvard.edu <freesurfer-request(a)nmr.mgh.harvard.edu> Sent: Monday, March 2, 2020 12:00 PM To: freesurfer(a)nmr.mgh.harvard.edu <freesurfer(a)nmr.mgh.harvard.edu> Subject: [EXTERNAL] Freesurfer Digest, Vol 193, Issue 3 Send Freesurfer mailing list submissions to freesurfer(a)nmr.mgh.harvard.edu To subscribe or unsubscribe via the World Wide Web, visit https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.nmr.mgh.harvard.e… or, via email, send a message with subject or body 'help' to freesurfer-request(a)nmr.mgh.harvard.edu You can reach the person managing the list at freesurfer-owner(a)nmr.mgh.harvard.edu When replying, please edit your Subject line so it is more specific than "Re: Contents of Freesurfer digest..."

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Issue with make_average_subject
by imurrieta＠hsctmexico.com 02 Mar '20

02 Mar '20

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recon-all-failure with file links
by Ian 02 Mar '20

02 Mar '20

External Email - Use Caution Oops, looks like the files didn't attach. Here are links to the logs: https://obd-public.s3.eu-west-2.amazonaws.com/recon-all-standard_out_log.log https://obd-public.s3.eu-west-2.amazonaws.com/recon-all-status.log https://obd-public.s3.eu-west-2.amazonaws.com/recon-all.log Hi all. I have a T1 scan which I run through recon-all with: recon-all -all -parallel -subjid freesurfer (after mksubjdirs etc) On my office machine, recon-all completes successfully. On a cloud machine (16GB of ram), it fails after about 4 hours. I have attached the logs - the only immediate error I can see is "Cannot find rh.white.H". Any advice would be much appreciated, thank you. Ian -- *Ian Hardingham* Chief Technology Officer signature_873986710 ian.hardingham(a)oxfordbraindiagnostics.com <mailto:terry.pollard@oxfordbraindiagnostics.com> +44 (0)7789 770555 Oxford Centre for Innovation | New Road | Oxford | OX1 1BY Oxford Brain Diagnostics Ltd is a company registered in England and Wales | Company No. 11703979 | VAT No. 321 3026 62

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DPABI V4.3, DPABISurf V1.3 and DPARSF V5.0 were released!
by YAN Chao-Gan 02 Mar '20

02 Mar '20

External Email - Use Caution Dear colleagues, We are pleased to announce the release of DPABI V4.3, DPABISurf V1.3 and DPARSF V5.0. New features of DPABI_V4.3_200301 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi): 1. Stability Analysis module was added. You can calculate volume-based and surface-based stability from DPABI->Dynamic & Stability Analyses. You can read our recent work for the details of stability measure: Li, L., Lu, B., Yan, C.G. (2019). Stability of dynamic functional architecture differs between brain networks and states. Neuroimage, 116230, doi:10.1016/j.neuroimage.2019.116230. 2. Field map correction was added, both for DPARSF and DPABISurf. If you want to perform field map Correction, you need to arrange each subject's field map DICOM files in one directory, and then put them in "FieldMap" directory under the working directory. i.e.:';... '{Working Directory}\FieldMap\PhaseDiffRaw\Subject001\xxxxx001.dcm';... '{Working Directory}\FieldMap\PhaseDiffRaw\Subject001\xxxxx002.dcm';... '...';... '{Working Directory}\FieldMap\PhaseDiffRaw\Subject002\xxxxx001.dcm';... '{Working Directory}\FieldMap\PhaseDiffRaw\Subject002\xxxxx002.dcm';... '...';... '{Working Directory}\FieldMap\Magnitude1Raw\Subject001\xxxxx001.dcm';... '{Working Directory}\FieldMap\Magnitude1Raw\Subject001\xxxxx002.dcm';... '...';... '{Working Directory}\FieldMap\Magnitude1Raw\Subject002\xxxxx001.dcm';... '{Working Directory}\FieldMap\Magnitude1Raw\Subject002\xxxxx002.dcm';... '...';... '...';... Then you can click the button of “FieldMap” button to set field map correction parameters. In most cases, you can use the default “0” value to let the program read the parameters (e.g., echo times) from the DICOM files. 3. Check data organization function added. For the new users of DPARSF and DPABISurf, most of the errors were caused by data organization! Please use DPABI->Utilities->Check Data Organization to check your data organization before running DPARSF or DPABISurf. This program will lead you to organize your data correctly with prompting messages! 4. Slice timing information read from DICOM files. If you are starting with DICOM files, you no longer need to set the slice timing correction parameters. Just leave it as default (slice number: 0), then DPARSF or DPABISurf will read the parameters from DICOM files. This new feature thanks to Dr. Chris Rorden's new dcm2niiX program (version v1.0.20190902). 5. DPARSF V5.0 now is compatible with BIDS format. You can start with BIDS format data by checking checkbox “BIDS to DPARSF” and setting “Starting Directory Name” to “BIDS”. 6. DPABISurf V1.3. Check and re-run fmriprep failed subjects. If for any reason, the program failed fmriprep running in DPABISurf, you just need to re-run starting with the step “Preprocessing with fmriprep” and set the “Starting Directory Name” to “BIDS” in DPABISurf_Pipeline. Alternatively, you can run a single checking step from DPABI->DPABISurf->Utilities->Re-Run fmriprep Failed Subjects. 7. DPABI->Utilities->DICOM Sorter. Now DICOM Sorter will remove illegal characters for file names in DICOM sorter. E.g., a subject id of “Wang’#$#’s” will no longer cause a problem in sorting DICOM files. 8. y_Call_DPABISurf_VIEW_FromVolume.m and y_Call_DPABISurf_VIEW.m were added. Now you can use script to call DPABISurf_VIEW to generate surface maps (with higher quality now) in batch. Remember to add “close all” in the for loop to prevent too many windows. Tips: 1) For Linux or Mac OS, please start matlab from terminal in order to reach docker in DPABI (e.g., Linux: matlab; Mac: open /Applications/MATLAB_R2018a.app/). 2) Before running DPABISurf_Pipeline, you can test the docker environment by running DPABI->DPABISurf->Utilities->Volume-Surface Projector. If the file can be successfully projected to surface, then the software is all set. Best, Chao-Gan -- Chao-Gan YAN, Ph.D. Professor, Principal Investigator Director, International Big-Data Center for Depression Research Deputy Director, Magnetic Resonance Imaging Research Center Institute of Psychology, Chinese Academy of Sciences 16 Lincui Road, Chaoyang District, Beijing 100101, China - Initiator <http://rfmri.org/DPARSF>DPABI <http://rfmri.org/DPABI> <http://rfmri.org/DPARSF>, <http://dpabi.org>DPARSF <http://rfmri.org/DPARSF>, PRN <http://rfmri.org/PRN> and The R-fMRI Network <http://rfmri.org> (RFMRI.ORG <http://rfmri.org/>) http://rfmri.org/yan http://scholar.google.com/citations?user=lJQ9B58AAAAJ

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PhD Position in Multilingualism/Psycholinguistics
by Narly Golestani 01 Mar '20

01 Mar '20

External Email - Use Caution With the launch of a new ‘NCCR Evolving Language’ (http://www.snf.ch/SiteCollectionDocuments/nfs/Faktenblatt_NCCR_evolvinglang…) which involves nearly 40 different research groups from a large variety of disciplines across Switzerland, we seek to fill a PhD position for a four-year project starting in May 2020, or at the selected candidate’s convenience. Our goal is to make progress in understanding language giftedness from cognitive, psychological and neural perspectives. To this end, we plan to carry out a set of studies involving both behavioral measures of cognitive and language-related abilities (e.g. auditory/perceptual abilities, phonemic perception and production, inductive learning, working memory etc) and brain imaging measures (fMRI and sMRI). The research group involves two PIs (Golestani, Geneva; and Berthele, Fribourg) and a postdoc. Data will be collected with Francophone (mono- and multilingual) subjects. Applicants should hold an MA degree in psychology, linguistics or a related field (e.g. psycholinguistics, applied linguistics, multillingualism), have a good command French and English and have strong quantitative research and statistics skills. Additional mastery of German and experience with neuroimaging techniques would be a strong advantage. The NCCR places great emphasis on gender diversity and we particularly encourage women to apply. Applicants may send a pdf file including a cover letter, a CV, names and contact information of 2-3 references as well as any other relevant documents to Narly.Golestani(a)unige.ch<mailto:Narly.Golestani@unige.ch> and Raphael.berthele(a)unifr.ch<mailto:Raphael.berthele@unifr.ch>. Deadline for application: March 31 2020 ----------------------------------------------------------------------------------------------------------------------------------------------------------------- Brain and Language Lab Prof. Narly GOLESTANI Department of Psychology Campus Biotech 9 Chemin des Mines, 1202 Genève +41 22 379 53 52 <https://outlook.unige.ch/owa/redir.aspx?SURL=5WTo-p-J-W9FrDydU-0WU4PdMytUGs…>http://www.brainandlanguagelab.org https://twitter.com/brain_lang_lab ----------------------------------------------------------------------------------------------------------------------------------------------------------------- <https://outlook.unige.ch/owa/redir.aspx?SURL=5WTo-p-J-W9FrDydU-0WU4PdMytUGs…>

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Postdoc position available - computational modeling and real-time fMRI neurofeedback of language
by Narly Golestani 01 Mar '20

01 Mar '20

External Email - Use Caution With the launch of a new ‘NCCR Evolving Language’ (http://www.snf.ch/SiteCollectionDocuments/nfs/Faktenblatt_NCCR_evolvinglang…) which involves nearly 40 different research groups from a large variety of disciplines across Switzerland, we seek to fill a Postdoc position for a four-year project starting in May 2020, or at the selected candidate’s earliest convenience. Our goal is to make progress in understanding the importance of prosody in language processing. To this end, we plan to carry out a set of studies involving both computational modeling of fMRI data and real-time fMRI neurofeedback to understand the mechanisms whereby prosody and rhythm support linguistic processing per se. The computational modeling work will be an extension of our recent work on the neural encoding of acoustic information in speech sounds (https://www.ncbi.nlm.nih.gov/pubmed/31285622) The research will be done in the group of Narly Golestani (www.brainandlanguagelab.org<http://www.brainandlanguagelab.org>) in Geneva, in collaboration with researchers in Switzerland and abroad (Elia Formisano in Maastricht, and Frank Scharnowski in Vienna). Applicants should hold a degree in Biomedical Engineering, Computational Neuroscience, or a related field, and should have solid experience in human brain imaging, and ideally also in computational modelling (i.e. encoding / decoding) of fMRI data, real-time fMRI based neurofeedback, machine learning, statistics and programming. The NCCR places great emphasis on gender diversity and we particularly encourage women to apply. To apply, please send a cover letter, CV, names and contact information of 2-3 references as well as any other relevant documents to Narly.Golestani(a)unige.ch<mailto:Narly.Golestani@unige.ch>. Feel free to contact me should you have any questions. Deadline for application: March 15th 2020 ----------------------------------------------------------------------------------------------------------------------------------------------------------------- Brain and Language Lab Prof. Narly GOLESTANI Department of Psychology Campus Biotech 9 Chemin des Mines, 1202 Genève +41 22 379 53 52 <https://outlook.unige.ch/owa/redir.aspx?SURL=5WTo-p-J-W9FrDydU-0WU4PdMytUGs…>http://www.brainandlanguagelab.org https://twitter.com/brain_lang_lab ----------------------------------------------------------------------------------------------------------------------------------------------------------------- <https://outlook.unige.ch/owa/redir.aspx?SURL=5WTo-p-J-W9FrDydU-0WU4PdMytUGs…>

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Want to display some region
by yyxqq 01 Mar '20

01 Mar '20

External Email - Use Caution Hello, FreeSurfer experts, I want to use freesurfer to draw a 3D brain image of a specific region like Right-fimbria and subiculum in a template space. I have look through some materials today, but not find any resolution. Can you do me a favor? Thank you very much! Best regards, Yang

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Re: [Freesurfer] Creating the pial surface of the cerebellum: how to let the mris_make_surfaces to push the pial surface to the boundary
by CiRong Liu 28 Feb '20

28 Feb '20

External Email - Use Caution Hi, Douglas, Thank you for your reply. I tried adding -max XXX. If I set the "-max 5" as the default values, the mris_mask_surfaces would finish running. However, if I set the "-max" more than 5, the mris_mask_surfaces would crash during the running - *Segmentation fault (core dumped).* *This is my computer config:* *1) Freesurfer Version - * freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c 2) Platform: Linux Mint 19.2 3) uname -a: Linux 4.15.0-60-generic #67-Ubuntu SMP Thu Aug 22 16:55:30 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux *Here is the error msg (at the end)for example:* $mris_make_surfaces -max 10 -max_csf 0.1 -min_gray_at_csf_border 1 -orig_wm orig -noaseg -noaparc -T1 ceb_sm marmosetceb_v2 rh using max_thickness = 10.0 reading original vertex positions from orig not using aseg volume to prevent surfaces crossing the midline not using aparc to prevent surfaces crossing the midline using ceb_sm as T1 volume... $Id: mris_make_surfaces.c,v 1.164.2.4 2016/12/13 22:26:32 zkaufman Exp $ $Id: mrisurf.c,v 1.781.2.6 2016/12/27 16:47:14 zkaufman Exp $ reading volume /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/mri/filled.mgz... reading volume /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/mri/ceb_sm.mgz... resampling filled volume to be in voxel register with T1 reading volume /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/mri/wm.mgz... changing type of input wm volume to UCHAR... resampling wm volume to be in voxel register with T1 0 bright wm thresholded. 0 bright non-wm voxels segmented. reading original surface position from /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/surf/rh.orig... computing class statistics... border white: 170215 voxels (0.34%) border gray 211849 voxels (0.42%) WM (79.0): 80.8 +- 7.5 [70.0 --> 106.0] GM (71.0) : 71.9 +- 8.9 [30.0 --> 105.0] using class modes intead of means, discounting robust sigmas.... intensity peaks found at WM=70+-10.4, GM=69+-10.4 setting MIN_GRAY_AT_WHITE_BORDER to 60.1 (was 70) setting MAX_BORDER_WHITE to 77.5 (was 105) setting MIN_BORDER_WHITE to 69.0 (was 85) setting MAX_CSF to 8.0 (was 40) setting MAX_GRAY to 62.5 (was 95) setting MAX_GRAY_AT_CSF_BORDER to 60.1 (was 75) setting MIN_GRAY_AT_CSF_BORDER to 8.0 (was 40) mean inside = 74.8, mean outside = 73.4 smoothing surface for 5 iterations... repositioning cortical surface to gray/white boundary smoothing T1 volume with sigma = 2.000 vertex spacing 0.87 +- 0.21 (0.01-->1.75) (max @ vno 190205 --> 190741) face area 0.31 +- 0.12 (0.00-->0.93) mean border=73.1, 45600 (45600) missing vertices, mean dist 2.1 [3.9 (%15.7)->2.6 (%84.3))] %31 local maxima, %44 large gradients and % 2 min vals, 0 gradients ignored mean absolute distance = 2.85 +- 2.92 7642 vertices more than 2 sigmas from mean. averaging target values for 5 iterations... tol=1.0e-04, sigma=2.0, host=unkno, nav=4, nbrs=2, l_repulse=5.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 complete_dist_mat 0 rms 0 smooth_averages 0 remove_neg 0 ico_order 0 which_surface 0 target_radius 0.000000 nfields 0 scale 0.000000 desired_rms_height 0.000000 momentum 0.000000 nbhd_size 0 max_nbrs 0 niterations 25 nsurfaces 0 SURFACES 3 flags 0 (0) use curv 0 no sulc 0 no rigid align 0 mris->nsize 2 mris->hemisphere 1 randomSeed 0 000: dt: 0.0000, sse=2428827.0, rms=6.800 001: dt: 0.5000, sse=2228267.0, rms=6.217 (8.575%) 002: dt: 0.5000, sse=2028263.5, rms=5.645 (9.199%) 003: dt: 0.5000, sse=1886400.0, rms=5.096 (9.731%) 004: dt: 0.5000, sse=1814327.0, rms=4.583 (10.072%) 005: dt: 0.5000, sse=1721364.9, rms=4.121 (10.063%) 006: dt: 0.5000, sse=1625707.9, rms=3.724 (9.648%) 007: dt: 0.5000, sse=1655070.8, rms=3.394 (8.851%) 008: dt: 0.5000, sse=1652526.4, rms=3.136 (7.621%) 009: dt: 0.5000, sse=1610117.9, rms=2.940 (6.224%) 010: dt: 0.5000, sse=1561510.9, rms=2.795 (4.945%) 011: dt: 0.5000, sse=1554297.9, rms=2.681 (4.083%) 012: dt: 0.5000, sse=1584733.6, rms=2.592 (3.329%) 013: dt: 0.5000, sse=1583861.1, rms=2.517 (2.880%) 014: dt: 0.5000, sse=1546283.4, rms=2.452 (2.578%) 015: dt: 0.5000, sse=1573124.0, rms=2.396 (2.291%) 016: dt: 0.5000, sse=1560157.1, rms=2.343 (2.203%) rms = 2.30, time step reduction 1 of 3 to 0.250... 017: dt: 0.5000, sse=1584955.1, rms=2.296 (2.021%) rms = 2.27, time step reduction 2 of 3 to 0.125... 018: dt: 0.2500, sse=1413241.2, rms=2.266 (1.279%) rms = 2.25, time step reduction 3 of 3 to 0.062... 019: dt: 0.1250, sse=1348002.0, rms=2.251 (0.692%) positioning took 2.0 minutes smoothing T1 volume with sigma = 1.000 vertex spacing 0.81 +- 0.26 (0.02-->3.05) (max @ vno 1015 --> 1374) face area 0.31 +- 0.17 (0.00-->2.54) mean border=73.2, 37029 (33580) missing vertices, mean dist -0.4 [1.3 (%54.3)->0.9 (%45.7))] %45 local maxima, %34 large gradients and % 3 min vals, 0 gradients ignored mean absolute distance = 1.13 +- 2.38 7372 vertices more than 2 sigmas from mean. averaging target values for 5 iterations... tol=1.0e-04, sigma=1.0, host=unkno, nav=2, nbrs=2, l_repulse=5.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=1247019.2, rms=2.658 020: dt: 0.5000, sse=1660886.4, rms=2.513 (5.452%) 021: dt: 0.5000, sse=1696660.5, rms=2.429 (3.370%) 022: dt: 0.5000, sse=1582424.1, rms=2.353 (3.102%) 023: dt: 0.5000, sse=1592837.6, rms=2.296 (2.425%) 024: dt: 0.5000, sse=1575169.2, rms=2.246 (2.202%) rms = 2.20, time step reduction 1 of 3 to 0.250... 025: dt: 0.5000, sse=1552572.5, rms=2.202 (1.924%) rms = 2.18, time step reduction 2 of 3 to 0.125... 026: dt: 0.2500, sse=1465932.9, rms=2.178 (1.103%) rms = 2.16, time step reduction 3 of 3 to 0.062... 027: dt: 0.1250, sse=1308048.4, rms=2.165 (0.619%) positioning took 0.9 minutes smoothing T1 volume with sigma = 0.500 vertex spacing 0.80 +- 0.27 (0.03-->3.21) (max @ vno 1015 --> 1374) face area 0.26 +- 0.15 (0.00-->2.57) mean border=73.1, 36657 (30198) missing vertices, mean dist -0.2 [1.2 (%53.9)->0.9 (%46.1))] %64 local maxima, %15 large gradients and % 2 min vals, 0 gradients ignored mean absolute distance = 1.05 +- 1.90 5676 vertices more than 2 sigmas from mean. averaging target values for 5 iterations... tol=1.0e-04, sigma=0.5, host=unkno, nav=1, nbrs=2, l_repulse=5.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=1326640.1, rms=2.462 028: dt: 0.5000, sse=1621191.5, rms=2.312 (6.099%) 029: dt: 0.5000, sse=1624567.6, rms=2.238 (3.187%) 030: dt: 0.5000, sse=1600156.1, rms=2.183 (2.467%) rms = 2.14, time step reduction 1 of 3 to 0.250... 031: dt: 0.5000, sse=1617027.1, rms=2.144 (1.794%) rms = 2.12, time step reduction 2 of 3 to 0.125... 032: dt: 0.2500, sse=1457184.9, rms=2.121 (1.087%) rms = 2.11, time step reduction 3 of 3 to 0.062... 033: dt: 0.1250, sse=1341353.0, rms=2.109 (0.564%) positioning took 0.7 minutes smoothing T1 volume with sigma = 0.250 vertex spacing 0.79 +- 0.27 (0.03-->3.02) (max @ vno 166101 --> 167636) face area 0.25 +- 0.14 (0.00-->2.52) mean border=73.0, 36288 (28156) missing vertices, mean dist -0.2 [1.0 (%53.1)->0.8 (%46.9))] %73 local maxima, % 7 large gradients and % 2 min vals, 0 gradients ignored mean absolute distance = 0.93 +- 1.64 5026 vertices more than 2 sigmas from mean. averaging target values for 5 iterations... tol=1.0e-04, sigma=0.2, host=unkno, nav=0, nbrs=2, l_repulse=5.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=1345812.6, rms=2.264 034: dt: 0.5000, sse=1665688.5, rms=2.172 (4.051%) rms = 2.13, time step reduction 1 of 3 to 0.250... 035: dt: 0.5000, sse=1612717.9, rms=2.133 (1.821%) rms = 2.11, time step reduction 2 of 3 to 0.125... 036: dt: 0.2500, sse=1435326.8, rms=2.109 (1.107%) rms = 2.10, time step reduction 3 of 3 to 0.062... 037: dt: 0.1250, sse=1305283.9, rms=2.097 (0.555%) positioning took 0.5 minutes writing white matter surface to /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/surf/rh.white... writing smoothed curvature to rh.curv writing curvature file /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/surf/rh.curv writing smoothed area to rh.area writing curvature file /home/cirong/bin/freesurfer/subjects/marmosetceb_v2/surf/rh.area vertex spacing 0.79 +- 0.27 (0.01-->3.24) (max @ vno 1015 --> 1374) face area 0.24 +- 0.14 (0.00-->2.12) smoothing surface for 5 iterations... repositioning cortical surface to gray/csf boundary. smoothing T1 volume with sigma = 2.000 mean border=46.6, 59907 (59907) missing vertices, mean dist 10.2 [0.0 (%0.0)->14.1 (%100.0))] %23 local maxima, % 7 large gradients and %41 min vals, 5938 gradients ignored averaging target values for 5 iterations... perforing initial smooth deformation to move away from white surface tol=1.0e-04, sigma=2.0, host=unkno, nav=16, nbrs=2, l_surf_repulse=1.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.05 000: dt: 0.0000, sse=22236570.0, rms=27.443 001: dt: 0.0500, sse=21651240.0, rms=27.069 (1.363%) 002: dt: 0.0500, sse=21290688.0, rms=26.835 (0.862%) 003: dt: 0.0500, sse=21029676.0, rms=26.665 (0.635%) 004: dt: 0.0500, sse=20819052.0, rms=26.527 (0.519%) 005: dt: 0.0500, sse=20636676.0, rms=26.406 (0.454%) 006: dt: 0.0500, sse=20471550.0, rms=26.296 (0.415%) 007: dt: 0.0500, sse=20317400.0, rms=26.194 (0.391%) 008: dt: 0.0500, sse=20170454.0, rms=26.095 (0.375%) 009: dt: 0.0500, sse=20028784.0, rms=26.000 (0.365%) 010: dt: 0.0500, sse=19890884.0, rms=25.907 (0.358%) positioning took 1.0 minutes mean border=46.4, 57828 (53089) missing vertices, mean dist 9.6 [0.2 (%0.0)->13.3 (%100.0))] %24 local maxima, % 7 large gradients and %40 min vals, 4827 gradients ignored tol=1.0e-04, sigma=2.0, host=unkno, nav=16, nbrs=2, l_surf_repulse=1.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.05 000: dt: 0.0000, sse=21510736.0, rms=26.320 011: dt: 0.0500, sse=21370844.0, rms=26.231 (0.338%) 012: dt: 0.0500, sse=21233808.0, rms=26.144 (0.333%) 013: dt: 0.0500, sse=21098910.0, rms=26.057 (0.331%) 014: dt: 0.0500, sse=20965742.0, rms=25.972 (0.329%) 015: dt: 0.0500, sse=20834178.0, rms=25.887 (0.327%) 016: dt: 0.0500, sse=20703918.0, rms=25.802 (0.326%) 017: dt: 0.0500, sse=20575078.0, rms=25.719 (0.324%) 018: dt: 0.0500, sse=20447296.0, rms=25.635 (0.324%) 019: dt: 0.0500, sse=20320634.0, rms=25.553 (0.323%) 020: dt: 0.0500, sse=20195002.0, rms=25.470 (0.323%) positioning took 1.1 minutes mean border=46.3, 57994 (51335) missing vertices, mean dist 9.3 [0.1 (%0.0)->12.8 (%100.0))] %24 local maxima, % 7 large gradients and %40 min vals, 4820 gradients ignored tol=1.0e-04, sigma=2.0, host=unkno, nav=16, nbrs=2, l_surf_repulse=1.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.05 000: dt: 0.0000, sse=20284860.0, rms=25.363 021: dt: 0.0500, sse=20159524.0, rms=25.281 (0.323%) 022: dt: 0.0500, sse=20035252.0, rms=25.199 (0.323%) 023: dt: 0.0500, sse=19911852.0, rms=25.118 (0.322%) 024: dt: 0.0500, sse=19789384.0, rms=25.037 (0.322%) 025: dt: 0.0500, sse=19667922.0, rms=24.957 (0.322%) 026: dt: 0.0500, sse=19547232.0, rms=24.877 (0.322%) 027: dt: 0.0500, sse=19427442.0, rms=24.797 (0.321%) 028: dt: 0.0500, sse=19308560.0, rms=24.717 (0.321%) 029: dt: 0.0500, sse=19190536.0, rms=24.638 (0.321%) 030: dt: 0.0500, sse=19073444.0, rms=24.559 (0.320%) positioning took 1.2 minutes mean border=46.3, 58182 (49681) missing vertices, mean dist 9.0 [0.1 (%0.1)->12.4 (%99.9))] %24 local maxima, % 7 large gradients and %40 min vals, 4842 gradients ignored tol=1.0e-04, sigma=2.0, host=unkno, nav=16, nbrs=2, l_surf_repulse=5.000, l_tspring=1.000, l_nspring=0.500, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=19160218.0, rms=24.467 031: dt: 0.5000, sse=18407090.0, rms=23.957 (2.084%) 032: dt: 0.5000, sse=17688926.0, rms=23.460 (2.077%) 033: dt: 0.5000, sse=17006668.0, rms=22.976 (2.063%) 034: dt: 0.5000, sse=16360864.0, rms=22.507 (2.040%) 035: dt: 0.5000, sse=15751547.0, rms=22.055 (2.009%) 036: dt: 0.5000, sse=15177610.0, rms=21.620 (1.973%) 037: dt: 0.5000, sse=14637857.0, rms=21.201 (1.936%) 038: dt: 0.5000, sse=14127666.0, rms=20.797 (1.904%) 039: dt: 0.5000, sse=13644749.0, rms=20.407 (1.877%) 040: dt: 0.5000, sse=13185130.0, rms=20.028 (1.856%) 041: dt: 0.5000, sse=12746617.0, rms=19.660 (1.838%) 042: dt: 0.5000, sse=12325521.0, rms=19.300 (1.832%) 043: dt: 0.5000, sse=11921329.0, rms=18.948 (1.825%) 044: dt: 0.5000, sse=11532627.0, rms=18.603 (1.819%) 045: dt: 0.5000, sse=11158549.0, rms=18.265 (1.816%) 046: dt: 0.5000, sse=10795637.0, rms=17.932 (1.826%) 047: dt: 0.5000, sse=10442737.0, rms=17.601 (1.845%) 048: dt: 0.5000, sse=10097535.0, rms=17.271 (1.871%) 049: dt: 0.5000, sse=9762229.0, rms=16.945 (1.887%) 050: dt: 0.5000, sse=9437930.0, rms=16.624 (1.896%) 051: dt: 0.5000, sse=9120953.0, rms=16.304 (1.927%) 052: dt: 0.5000, sse=8813598.0, rms=15.987 (1.940%) 053: dt: 0.5000, sse=8514504.0, rms=15.673 (1.965%) 054: dt: 0.5000, sse=8222812.0, rms=15.361 (1.991%) 055: dt: 0.5000, sse=7938257.0, rms=15.051 (2.021%) 056: dt: 0.5000, sse=7662795.0, rms=14.744 (2.040%) Segmentation fault (core dumped) Data were here: https://pitt.box.com/s/6269a9mnwbs6zi0azn08dn55u47yfa7i ------------------------ Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from…> Mon, 24 Feb 2020 06:34:51 -0800 <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date…> Try setting -max XXX where XXX is the maximum allowable thickness. It is default to 5, so try something large On 2/21/2020 11:36 AM, CiRong Liu wrote: External Email - Use Caution Dear experts of the freesurfer, I was trying to create the white matter and pial surfaces of the cerebellum of the marmoset, based on ultra-high (80um) resolution ex-vivo images. I manually segment the cerebellum into the white matter and the gray matter and change the header information to 1mm. Based on the manual segmented files, I created white matter surfaces (rh.orig). I tried to use the mris_make_surfaces to expand the white matter to create the matched pial surface. However, the /mris_make_surfaces/ failed to push the surface enough. See the attached files and screenshots. I tried smoothing the segmentation, enhancing the contrasts, and tested different expert option (for example: -max_csf 0.1 -min_gray_at_csf_border 1) This was this best I got, but still cannot get an optimal result (not pushed enough): /mris_mask_surfaces -max_csf 0.1 -min_gray_at_csf_border 1 -orig_wm orig -orig_pial orig -noaseg -noaparc -T1 ceb_sm marmosetceb rh / Since I have already manually segmented the cerebellum, *is there a way to force the /mris_make_surfaces/ to expand to the boundary of the image?* The files can be download to replicate what I did:https://pitt.box.com/s/6269a9mnwbs6zi0azn08dn55u47yfa7i Thank you very much! Best Regards, CiRong Liu _______________________________________________ Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _____________________________________ Best Regards, CiRong Liu

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