Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30129 discussions

Voxel editing
by Yoel HALIMI 22 Mar '25

22 Mar '25

External Email - Use Caution Hello, I have a problem with the tool: Voxel editing. When I open the window, it appears correctly but I can't modify it or enter values. I'm using WSL 2 and I've followed the tutorial. When I move my mouse over it, the cursor is transparent. Here an image to describe my problem : [cid:442d656e-f59b-46db-a88f-5154ebafb784] Thanks to reply, Best regards, Yoel

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thresholding advice for probabilistic subfield amygdala atlas in MNI 152 space
by Stephanie Williams 20 Mar '25

20 Mar '25

External Email - Use Caution Hello, When I threshold the new probabilistic maps amygdala subnuclei in MN152 space at a threshold of 0.8 I see that only 5 amygdala subnuclei survive (AAA, Basal nucleus, Corticoamygdaloid transition, Lateral nucleus, Accessory Basal nucleus). Is this perhaps overly strict threshold? Are there any general consensus guidelines for picking a reasonable threshold to use with these probabilistic maps? When I use a lower threshold of 0.35 there is still one nuclei that does not survive (medial nucleus). Thank you!! Location of maps: https://secure-web.cisco.com/1uhXMhTQwjvX1lxAZWJI1ov3nuo7OX-qQSRwkAXWXvSpar… HippoAmygProbs.MNIsymSpace.right.nii.gz 2025-03-03 11:28:52 13506796 HippoAmygProbs.MNIsymSpace.names.txt 2025-02-28 15:52:58 581 HippoAmygProbs.MNIsymSpace.left.nii.gz 2025-02-28 18:02:02 13506619

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Recon-all-clinical pipeline
by Lieshout, P.B. van (Pauline) 20 Mar '25

20 Mar '25

External Email - Use Caution Dear sir/madam, I am implementing the recon-all-clinical pipeline on clinical MRI scans. I have done a test-run of around 70 T1-w clinical MRI scans. Processing of 9 out of 70 scans have failed. In almost all the failed processing scans, the pipeline stopped at the corpus callosum segmentation step. I was curious as to why this might happen, do you have any ideas? error: mri_cc: no WM voxels found with norm > 40 -- check skull stripping Best, Pauline van Lieshout [A blue and white logo Description automatically generated] Promovendus | Divisie Hersenen, Zorglijn NMZ | Universitair Medisch Centrum Utrecht De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht ontvangt, wordt u verzocht de inhoud niet te gebruiken en de afzender direct te informeren door het bericht te retourneren. Het Universitair Medisch Centrum Utrecht is een publiekrechtelijke rechtspersoon in de zin van de W.H.W. (Wet Hoger Onderwijs en Wetenschappelijk Onderzoek) en staat geregistreerd bij de Kamer van Koophandel voor Midden-Nederland onder nr. 30244197. ________________________________ De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht ontvangt, wordt u verzocht de inhoud niet te gebruiken en de afzender direct te informeren door het bericht te retourneren. Het Universitair Medisch Centrum Utrecht is een publiekrechtelijke rechtspersoon in de zin van de W.H.W. (Wet Hoger Onderwijs en Wetenschappelijk Onderzoek) en staat geregistreerd bij de Kamer van Koophandel voor Midden-Nederland onder nr. 30244197. Denk s.v.p aan het milieu voor u deze e-mail afdrukt. ________________________________ This message may contain confidential information and is intended exclusively for the addressee. If you receive this message unintentionally, please do not use the contents but notify the sender immediately by return e-mail. University Medical Center Utrecht is a legal person by public law and is registered at the Chamber of Commerce for Midden-Nederland under no. 30244197. Please consider the environment before printing this e-mail.

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Re: [Freesurfer] Human connectome workbench
by Glasser, Matthew 18 Mar '25

18 Mar '25

External Email - Use Caution 1. A 512*512*90 dataset is likely a long way from an isotropic resolution dataset of 1mm isotropic or better that works best with FreeSurfer. 2. What Connectome Workbench command were you trying to run? Matt. From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of "Ni, Zhen (NIH/NINDS) [C]" <zhen.ni(a)nih.gov> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Tuesday, March 18, 2025 at 5:40 AM To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Human connectome workbench Hello, I used the command “recon-all -all -subjid $SUBJECTNAME”. There was no error for Freesurfer, but it was slow at certain time points with a lot of corrections (sometimes with large defects, our initial imaging was a 512*512*90 slices T1 axial imaging set). The outputs for this were used in HCP workbench to downsample the size of the cortical sheet. When I run the workbench script, it showed an error: While running >>>>/workbench/sub-001.L.sulc.native.shape.gii CORTEX_LEFT ERROR: NAME OF FILE: sub-002.L.sulc.native.shape.gii PATH TO FILE: >>>>/sub-002/workbench File does not exist I don’t think this is a problem with workbench but should be missing some components (cortical mesh) when the initial cortical sheet was made by Freesurfer. I wonder if you have any ideas. Thanks Zhen ________________________________ The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return mail.

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Human connectome workbench
by Ni, Zhen (NIH/NINDS) [C] 18 Mar '25

18 Mar '25

External Email - Use Caution Hello, I used the command "recon-all -all -subjid $SUBJECTNAME". There was no error for Freesurfer, but it was slow at certain time points with a lot of corrections (sometimes with large defects, our initial imaging was a 512*512*90 slices T1 axial imaging set). The outputs for this were used in HCP workbench to downsample the size of the cortical sheet. When I run the workbench script, it showed an error: While running >>>>/workbench/sub-001.L.sulc.native.shape.gii CORTEX_LEFT ERROR: NAME OF FILE: sub-002.L.sulc.native.shape.gii PATH TO FILE: >>>>/sub-002/workbench File does not exist I don't think this is a problem with workbench but should be missing some components (cortical mesh) when the initial cortical sheet was made by Freesurfer. I wonder if you have any ideas. Thanks Zhen

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Unusual Cortical Thickness Measurements
by hmarzban＠student.ubc.ca 17 Mar '25

17 Mar '25

External Email - Use Caution Hello, I hope you are well. I was wondering if there is an easy way to correct for missegmentation of the grey matter parcellation. For example, would it be possible to correct for the overestimation of the pre-postcentral gyri (see picture below)? Since it seems as if the parcellation is extending into the meninges. Warmly, Hengameh

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Hippocampal Subfield Hierarchies
by Erin L Ratliff 17 Mar '25

17 Mar '25

External Email - Use Caution Hi FS experts, We are running the segmentHA_T2.sh script, and we get the .mgz files for the 3 hierarchies (CA, FS60, and HBT), but when we run either concatenatesubregions or asegstats2table, we get the fine grain hippocampal subfield volumes. Is it possible to extract the volumes (as .txt) from the hierarchy .mgz files? We are interested in using the CA hierarchy, and we are not sure how to calculate this with the output we receive from either concatenatesubregions/asegstats2table. Any help is appreciated! Erin

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EasyReg/SynthSeg singular matrix error
by Suresh, Krish 17 Mar '25

17 Mar '25

External Email - Use Caution Hi, Thank you in advance for your assistance, I am new to freesurfer. I am trying to use EasyReg to register an axial T1 post-contrast series and an axial high-resolution T2 series, so that I can propagate a tumor segmentation from the T1 to the T2. I am getting the error message copied below. One problem I see is that the SynthSeg segmentation is not accurate, see screenshot. I am running freesurfer version 7.4.1 on WSL 2/Ubuntu 22. I would appreciate any pointers you may have, thank you very much. Krish mri_easyreg --ref t1.nii --flo t2.nii --ref_seg t1_seg.nii --flo_seg t2_seg.nii --ref_reg t1_reg.nii --flo_reg t2_reg.nii --fwd_field fwd_field.nii --bak_field bak_field.nii --threads -1 2025-03-16 21:49:05.684219: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-03-16 21:49:05.687469: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2025-03-16 21:49:05.728495: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2025-03-16 21:49:05.729021: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-03-16 21:49:06.476538: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT using all available threads ( 12 ) Segmenting reference image Reading reference image Setting up segmentation net Inference / segmentation 1/1 [==============================] - 11s 11s/step Postprocessing Saving result Segmenting floating image Reading floating image Inference / segmentation 1/1 [==============================] - 19s 19s/step Postprocessing Saving result Linear registration Computing centroids and estimating affine transform Traceback (most recent call last): File "/usr/local/freesurfer/7.4.1/python/scripts/mri_easyreg", line 1809, in <module> main() File "/usr/local/freesurfer/7.4.1/python/scripts/mri_easyreg", line 180, in main Mref = getM(atlasCOG[:, ok > 0], refCOG[:, ok > 0]) File "/usr/local/freesurfer/7.4.1/python/scripts/mri_easyreg", line 1198, in getM x = np.matmul(np.linalg.inv(np.matmul(np.transpose(A), A)), np.matmul(np.transpose(A), b)) File "<__array_function__ internals>", line 180, in inv File "/usr/local/freesurfer/7.4.1/python/lib/python3.8/site-packages/numpy/linalg/linalg.py", line 552, in inv ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj) File "/usr/local/freesurfer/7.4.1/python/lib/python3.8/site-packages/numpy/linalg/linalg.py", line 89, in _raise_linalgerror_singular raise LinAlgError("Singular matrix") numpy.linalg.LinAlgError: Singular matrix [cid:546a1e09-0300-496f-9c0d-58eb45c8e243]

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aparc2aseg not working
by Jin Liu 17 Mar '25

17 Mar '25

External Email - Use Caution Hello FreeSurfer team, I’m trying to create a lobe segmentation for white matter. I first created the annotation files using mri_annotation2label with --lobesStrict for each hemisphere, and then tried to map the lobe parcellation onto segmentation volume by using mri_aparc2aseg. But somehow the resulting image was not segmented by lobes. It contains cortical parcellation by lobe, detailed subcortical segmentation by structure, and white matter is marked as a whole (cerebral white matter). I then checked the annotation files created by lobesStrict and they look fine, without any subcortical labels. I wonder where I made a mistake, or if there is an easier way to do lobe segmentation. The code I used is as follows: mri_annotation2label --subject 1300 --hemi lh --labelbase ./1300/label/aparc-lh --lobesStrict ./1300/label/lh.aparc.lobesStrict mri_annotation2label --subject 1300 --hemi rh --labelbase ./1300/label/aparc-rh --lobesStrict ./1300/label/rh.aparc.lobesStrict mri_aparc2aseg --s 1300 --annot aparc.lobesStrict --o ./1300/mri/lobesStrict.mgz Best, Jin

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Recon-all timing with fs v.8.0 and parallel recon-all
by Paola Di Filippo 14 Mar '25

14 Mar '25

External Email - Use Caution Hi everyone. I am using Freesurfer v.8.0 on a MacBook Pro with M4 Pro Chip, 48 GB RAM, with MacOs Sequoia 15.2. I tried performing a couple of recon-all, they were completed without hard failures but with the following timing: recon-all-run-time-hours 13.590 recon-all-run-time-hours 8.973 I was wondering if considering my hardware characteristics and the FS version I am using, this is normal or there’s something that I am doing wrong. I have attached my log of both recons. Also, is the following command (Im aware it requires both Xcode and parallel installation) still valid in order to perform recon-all of multiple subjects at the same time with FS v. 8.0? How many subjects would you recommend to analyze simultaneously, and what kind of timing should I be expecting, considering that I have 14 logical cores? Sorry about the many questions and many thanks in advance ls *.nii | parallel --jobs 8 recon-all -s {.} -i {} -all -qcache --

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