Closed isacdaavid closed 7 months ago
Sometimes we get a different error. It seems like cp
fails copying a file (filled.mgz
), also during the recon-all phase of freesurfer:
File: /out/sub-CH8698b/log/20240205-022330_fd0d60cc-3c45-4f6f-9346-9bb73e5d702d/crash-20240205-032115-root-autorecon2_vol-0482c74c-54d6-4802-a38b-5041180a18fe.txt
Working Directory: /tmp/work/fmriprep_23_2_wf/sub_CH8698b_wf/anat_fit_wf/surface_recon_wf/autorecon_resume_wf/autorecon2_vol
Inputs:
FLAIR_file:
T1_files:
T2_file:
args:
big_ventricles:
brainstem:
directive: autorecon2-volonly
environ: {}
expert:
flags:
hemi:
hippocampal_subfields_T1:
hippocampal_subfields_T2:
hires:
mprage:
mri_aparc2aseg:
mri_ca_label:
mri_ca_normalize:
mri_ca_register:
mri_edit_wm_with_aseg:
mri_em_register:
mri_fill:
mri_mask:
mri_normalize:
mri_pretess:
mri_remove_neck:
mri_segment:
mri_segstats:
mri_tessellate:
mri_watershed:
mris_anatomical_stats:
mris_ca_label:
mris_fix_topology:
mris_inflate:
mris_make_surfaces:
mris_register:
mris_smooth:
mris_sphere:
mris_surf2vol:
mrisp_paint:
openmp: 8
parallel:
steps:
subject_id: recon_all
subjects_dir:
talairach:
use_FLAIR:
use_T2:
xopts:
Traceback (most recent call last):
File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/nipype/pipeline/plugins/multiproc.py", line 67, in run_node
result["result"] = node.run(updatehash=updatehash)
File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/nipype/pipeline/engine/nodes.py", line 527, in run
result = self._run_interface(execute=True)
File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/nipype/pipeline/engine/nodes.py", line 645, in _run_interface
return self._run_command(execute)
File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/nipype/pipeline/engine/nodes.py", line 771, in _run_command
raise NodeExecutionError(msg)
nipype.pipeline.engine.nodes.NodeExecutionError: Exception raised while executing Node autorecon2_vol.
Cmdline:
recon-all -autorecon2-volonly -openmp 8 -subjid sub-CH8698b -sd /out/sourcedata/freesurfer -nogcareg -nocanorm -nocareg
Stdout:
fs-check-version --s sub-CH8698b --o /tmp/tmp.zS155I
Mon Feb 5 02:39:30 UTC 2024
setenv SUBJECTS_DIR /out/sourcedata/freesurfer
cd /tmp/work/fmriprep_23_2_wf/sub_CH8698b_wf/anat_fit_wf/surface_recon_wf/autorecon_resume_wf/autorecon2_vol
/opt/freesurfer/bin/fs-check-version --s sub-CH8698b --o /tmp/tmp.zS155I
-rwxrwxr-x 1 root root 18565 Aug 4 2022 /opt/freesurfer/bin/fs-check-version
freesurfer-linux-ubuntu22_x86_64-7.3.2-20220804-6354275
$Id$
Linux a6080e14df56 5.4.0-169-generic #187-Ubuntu SMP Thu Nov 23 14:52:28 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
pid 698
Current FS Version freesurfer-linux-ubuntu22_x86_64-7.3.2-20220804-6354275
bstampfile exists /out/sourcedata/freesurfer/sub-CH8698b/scripts/build-stamp.txt
Subject FS Version: freesurfer-linux-ubuntu22_x86_64-7.3.2-20220804-6354275
No constraints on version because REQ=UnSet and FsVerFile=NotThere
#@#% fs-check-version match = 1
fs-check-version Done
INFO: SUBJECTS_DIR is /out/sourcedata/freesurfer
Actual FREESURFER_HOME /opt/freesurfer
-rw-rw-r-- 1 root root 246032 Jan 29 11:51 /out/sourcedata/freesurfer/sub-CH8698b/scripts/recon-all.log
Linux a6080e14df56 5.4.0-169-generic #187-Ubuntu SMP Thu Nov 23 14:52:28 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
/out/sourcedata/freesurfer/sub-CH8698b/mri/transforms /out/sourcedata/freesurfer/sub-CH8698b
/out/sourcedata/freesurfer/sub-CH8698b
#--------------------------------------
#@# SubCort Seg Mon Feb 5 02:39:32 UTC 2024
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /opt/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz
sysname Linux
hostname a6080e14df56
machine x86_64
setenv SUBJECTS_DIR /out/sourcedata/freesurfer
cd /out/sourcedata/freesurfer/sub-CH8698b/mri
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /opt/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz
relabeling unlikely voxels with window_size = 9 and prior threshold 0.30
using Gibbs prior factor = 0.500
renormalizing sequences with structure alignment, equivalent to:
-renormalize
-renormalize_mean 0.500
-regularize 0.500
== Number of threads available to for OpenMP = 8 ==
reading 1 input volumes
reading classifier array from /opt/freesurfer/average/RB_all_2020-01-02.gca
reading input volume from norm.mgz
average std[0] = 7.2
reading transform from transforms/talairach.m3z
setting orig areas to linear transform determinant scaled 5.33
Atlas used for the 3D morph was /opt/freesurfer/average/RB_all_2020-01-02.gca
average std = 7.2 using min determinant for regularization = 5.2
0 singular and 0 ill-conditioned covariance matrices regularized
labeling volume...
renormalizing by structure alignment....
renormalizing input #0
gca peak = 0.15521 (20)
mri peak = 0.10044 (12)
Left_Lateral_Ventricle (4): linear fit = 0.70 x + 0.0 (916 voxels, overlap=0.646)
Left_Lateral_Ventricle (4): linear fit = 0.70 x + 0.0 (916 voxels, peak = 14), gca=14.1
gca peak = 0.20380 (13)
mri peak = 0.14649 (18)
Right_Lateral_Ventricle (43): linear fit = 1.20 x + 0.0 (1514 voxels, overlap=0.743)
Right_Lateral_Ventricle (43): linear fit = 1.20 x + 0.0 (1514 voxels, peak = 16), gca=15.5
gca peak = 0.26283 (96)
mri peak = 0.21672 (98)
Right_Pallidum (52): linear fit = 1.02 x + 0.0 (646 voxels, overlap=0.932)
Right_Pallidum (52): linear fit = 1.02 x + 0.0 (646 voxels, peak = 98), gca=98.4
gca peak = 0.15814 (97)
mri peak = 0.23288 (97)
Left_Pallidum (13): linear fit = 1.00 x + 0.0 (657 voxels, overlap=0.947)
Left_Pallidum (13): linear fit = 1.00 x + 0.0 (657 voxels, peak = 97), gca=96.5
gca peak = 0.27624 (56)
mri peak = 0.21287 (67)
Right_Hippocampus (53): linear fit = 1.21 x + 0.0 (823 voxels, overlap=0.013)
Right_Hippocampus (53): linear fit = 1.21 x + 0.0 (823 voxels, peak = 67), gca=67.5
gca peak = 0.28723 (59)
mri peak = 0.13483 (65)
Left_Hippocampus (17): linear fit = 1.12 x + 0.0 (751 voxels, overlap=0.136)
Left_Hippocampus (17): linear fit = 1.12 x + 0.0 (751 voxels, peak = 66), gca=65.8
gca peak = 0.07623 (103)
mri peak = 0.26841 (106)
Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (32829 voxels, overlap=0.457)
Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (32829 voxels, peak = 105), gca=104.5
gca peak = 0.07837 (105)
mri peak = 0.21465 (105)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (35531 voxels, overlap=0.551)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (35531 voxels, peak = 104), gca=104.5
gca peak = 0.10165 (58)
mri peak = 0.04782 (66)
Left_Cerebral_Cortex (3): linear fit = 1.12 x + 0.0 (22966 voxels, overlap=0.645)
Left_Cerebral_Cortex (3): linear fit = 1.12 x + 0.0 (22966 voxels, peak = 65), gca=64.7
gca peak = 0.11113 (58)
mri peak = 0.05009 (63)
Right_Cerebral_Cortex (42): linear fit = 1.10 x + 0.0 (20600 voxels, overlap=0.669)
Right_Cerebral_Cortex (42): linear fit = 1.10 x + 0.0 (20600 voxels, peak = 64), gca=63.5
gca peak = 0.27796 (67)
mri peak = 0.13764 (78)
Right_Caudate (50): linear fit = 1.12 x + 0.0 (819 voxels, overlap=0.056)
Right_Caudate (50): linear fit = 1.12 x + 0.0 (819 voxels, peak = 75), gca=75.4
gca peak = 0.14473 (69)
mri peak = 0.16890 (77)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (821 voxels, overlap=0.674)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (821 voxels, peak = 69), gca=69.0
gca peak = 0.14301 (56)
mri peak = 0.07530 (66)
Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (22259 voxels, overlap=0.246)
Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (22259 voxels, peak = 65), gca=64.7
gca peak = 0.14610 (55)
mri peak = 0.07220 (68)
Right_Cerebellum_Cortex (47): linear fit = 1.21 x + 0.0 (23168 voxels, overlap=0.164)
Right_Cerebellum_Cortex (47): linear fit = 1.21 x + 0.0 (23168 voxels, peak = 66), gca=66.3
gca peak = 0.16309 (85)
mri peak = 0.25949 (91)
Left_Cerebellum_White_Matter (7): linear fit = 1.08 x + 0.0 (5523 voxels, overlap=0.018)
Left_Cerebellum_White_Matter (7): linear fit = 1.08 x + 0.0 (5523 voxels, peak = 91), gca=91.4
gca peak = 0.15172 (84)
mri peak = 0.17737 (90)
Right_Cerebellum_White_Matter (46): linear fit = 1.09 x + 0.0 (5334 voxels, overlap=0.068)
Right_Cerebellum_White_Matter (46): linear fit = 1.09 x + 0.0 (5334 voxels, peak = 91), gca=91.1
gca peak = 0.30461 (58)
mri peak = 0.16252 (72)
Left_Amygdala (18): linear fit = 1.24 x + 0.0 (378 voxels, overlap=0.036)
Left_Amygdala (18): linear fit = 1.24 x + 0.0 (378 voxels, peak = 72), gca=71.6
gca peak = 0.32293 (57)
mri peak = 0.14222 (70)
Right_Amygdala (54): linear fit = 1.23 x + 0.0 (450 voxels, overlap=0.040)
Right_Amygdala (54): linear fit = 1.23 x + 0.0 (450 voxels, peak = 70), gca=69.8
gca peak = 0.11083 (90)
mri peak = 0.08966 (92)
Left_Thalamus (10): linear fit = 1.03 x + 0.0 (4239 voxels, overlap=0.841)
Left_Thalamus (10): linear fit = 1.03 x + 0.0 (4239 voxels, peak = 93), gca=93.1
gca peak = 0.11393 (83)
mri peak = 0.09267 (94)
Right_Thalamus (49): linear fit = 1.11 x + 0.0 (3616 voxels, overlap=0.609)
Right_Thalamus (49): linear fit = 1.11 x + 0.0 (3616 voxels, peak = 92), gca=91.7
gca peak = 0.08575 (81)
mri peak = 0.13502 (83)
Left_Putamen (12): linear fit = 1.03 x + 0.0 (1573 voxels, overlap=0.577)
Left_Putamen (12): linear fit = 1.03 x + 0.0 (1573 voxels, peak = 84), gca=83.8
gca peak = 0.08618 (78)
mri peak = 0.11726 (83)
Right_Putamen (51): linear fit = 1.07 x + 0.0 (1825 voxels, overlap=0.761)
Right_Putamen (51): linear fit = 1.07 x + 0.0 (1825 voxels, peak = 83), gca=83.1
gca peak = 0.08005 (78)
mri peak = 0.10605 (90)
Brain_Stem (16): linear fit = 1.13 x + 0.0 (10876 voxels, overlap=0.366)
Brain_Stem (16): linear fit = 1.13 x + 0.0 (10876 voxels, peak = 89), gca=88.5
gca peak = 0.12854 (88)
mri peak = 0.13885 (100)
Right_VentralDC (60): linear fit = 1.11 x + 0.0 (1312 voxels, overlap=0.018)
Right_VentralDC (60): linear fit = 1.11 x + 0.0 (1312 voxels, peak = 97), gca=97.2
gca peak = 0.15703 (87)
mri peak = 0.16600 (100)
Left_VentralDC (28): linear fit = 1.11 x + 0.0 (1302 voxels, overlap=0.174)
Left_VentralDC (28): linear fit = 1.11 x + 0.0 (1302 voxels, peak = 96), gca=96.1
gca peak = 0.17522 (25)
mri peak = 0.33333 (21)
gca peak = 0.17113 (14)
mri peak = 0.28004 (20)
Fourth_Ventricle (15): linear fit = 1.33 x + 0.0 (117 voxels, overlap=0.283)
Fourth_Ventricle (15): linear fit = 1.33 x + 0.0 (117 voxels, peak = 19), gca=18.6
gca peak Unknown = 0.94777 ( 0)
gca peak Left_Inf_Lat_Vent = 0.16627 (28)
gca peak Third_Ventricle = 0.17522 (25)
gca peak CSF = 0.20346 (36)
gca peak Left_Accumbens_area = 0.70646 (62)
gca peak Left_undetermined = 1.00000 (28)
gca peak Left_vessel = 0.89917 (53)
gca peak Left_choroid_plexus = 0.11689 (35)
gca peak Right_Inf_Lat_Vent = 0.25504 (23)
gca peak Right_Accumbens_area = 0.31650 (65)
gca peak Right_vessel = 0.77268 (52)
gca peak Right_choroid_plexus = 0.13275 (38)
gca peak Fifth_Ventricle = 0.60973 (33)
gca peak WM_hypointensities = 0.11013 (77)
gca peak non_WM_hypointensities = 0.11354 (41)
gca peak Optic_Chiasm = 0.51646 (76)
not using caudate to estimate GM means
estimating mean gm scale to be 1.17 x + 0.0
estimating mean wm scale to be 1.00 x + 0.0
estimating mean csf scale to be 1.08 x + 0.0
saving intensity scales to aseg.auto_noCCseg.label_intensities.txt
renormalizing by structure alignment....
renormalizing input #0
gca peak = 0.19538 (13)
mri peak = 0.10044 (12)
Left_Lateral_Ventricle (4): linear fit = 1.03 x + 0.0 (916 voxels, overlap=0.966)
Left_Lateral_Ventricle (4): linear fit = 1.03 x + 0.0 (916 voxels, peak = 13), gca=13.5
gca peak = 0.16044 (15)
mri peak = 0.14649 (18)
Right_Lateral_Ventricle (43): linear fit = 0.98 x + 0.0 (1514 voxels, overlap=0.597)
Right_Lateral_Ventricle (43): linear fit = 0.98 x + 0.0 (1514 voxels, peak = 15), gca=14.6
gca peak = 0.21930 (98)
mri peak = 0.21672 (98)
Right_Pallidum (52): linear fit = 1.00 x + 0.0 (646 voxels, overlap=0.957)
Right_Pallidum (52): linear fit = 1.00 x + 0.0 (646 voxels, peak = 98), gca=98.0
gca peak = 0.16154 (96)
mri peak = 0.23288 (97)
Left_Pallidum (13): linear fit = 1.01 x + 0.0 (657 voxels, overlap=0.927)
Left_Pallidum (13): linear fit = 1.01 x + 0.0 (657 voxels, peak = 97), gca=97.4
gca peak = 0.27314 (67)
mri peak = 0.21287 (67)
Right_Hippocampus (53): linear fit = 0.99 x + 0.0 (823 voxels, overlap=0.782)
Right_Hippocampus (53): linear fit = 0.99 x + 0.0 (823 voxels, peak = 66), gca=66.0
gca peak = 0.32959 (63)
mri peak = 0.13483 (65)
Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (751 voxels, overlap=1.000)
Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (751 voxels, peak = 62), gca=62.1
gca peak = 0.07884 (105)
mri peak = 0.26841 (106)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (32829 voxels, overlap=0.508)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (32829 voxels, peak = 105), gca=105.0
gca peak = 0.08137 (104)
mri peak = 0.21465 (105)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (35531 voxels, overlap=0.539)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (35531 voxels, peak = 104), gca=104.0
gca peak = 0.08972 (65)
mri peak = 0.04782 (66)
Left_Cerebral_Cortex (3): linear fit = 1.00 x + 0.0 (22966 voxels, overlap=0.963)
Left_Cerebral_Cortex (3): linear fit = 1.00 x + 0.0 (22966 voxels, peak = 65), gca=65.0
gca peak = 0.10309 (64)
mri peak = 0.05009 (63)
Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (20600 voxels, overlap=0.944)
Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (20600 voxels, peak = 66), gca=65.6
gca peak = 0.22735 (75)
mri peak = 0.13764 (78)
Right_Caudate (50): linear fit = 1.00 x + 0.0 (819 voxels, overlap=1.000)
Right_Caudate (50): linear fit = 1.00 x + 0.0 (819 voxels, peak = 75), gca=75.0
gca peak = 0.14473 (69)
mri peak = 0.16890 (77)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (821 voxels, overlap=0.676)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (821 voxels, peak = 69), gca=69.0
gca peak = 0.12064 (65)
mri peak = 0.07530 (66)
Left_Cerebellum_Cortex (8): linear fit = 1.01 x + 0.0 (22259 voxels, overlap=0.949)
Left_Cerebellum_Cortex (8): linear fit = 1.01 x + 0.0 (22259 voxels, peak = 66), gca=66.0
gca peak = 0.12644 (67)
mri peak = 0.07220 (68)
Right_Cerebellum_Cortex (47): linear fit = 0.99 x + 0.0 (23168 voxels, overlap=0.976)
Right_Cerebellum_Cortex (47): linear fit = 0.99 x + 0.0 (23168 voxels, peak = 66), gca=66.0
gca peak = 0.15939 (91)
mri peak = 0.25949 (91)
Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (5523 voxels, overlap=0.746)
Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (5523 voxels, peak = 91), gca=91.0
gca peak = 0.14957 (92)
mri peak = 0.17737 (90)
Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5334 voxels, overlap=0.791)
Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5334 voxels, peak = 91), gca=90.6
gca peak = 0.22741 (72)
mri peak = 0.16252 (72)
Left_Amygdala (18): linear fit = 1.00 x + 0.0 (378 voxels, overlap=0.970)
Left_Amygdala (18): linear fit = 1.00 x + 0.0 (378 voxels, peak = 72), gca=72.0
gca peak = 0.24938 (70)
mri peak = 0.14222 (70)
Right_Amygdala (54): linear fit = 1.00 x + 0.0 (450 voxels, overlap=1.002)
Right_Amygdala (54): linear fit = 1.00 x + 0.0 (450 voxels, peak = 70), gca=70.0
gca peak = 0.09894 (93)
mri peak = 0.08966 (92)
Left_Thalamus (10): linear fit = 1.00 x + 0.0 (4239 voxels, overlap=0.944)
Left_Thalamus (10): linear fit = 1.00 x + 0.0 (4239 voxels, peak = 93), gca=92.5
gca peak = 0.10165 (89)
mri peak = 0.09267 (94)
Right_Thalamus (49): linear fit = 1.00 x + 0.0 (3616 voxels, overlap=0.934)
Right_Thalamus (49): linear fit = 1.00 x + 0.0 (3616 voxels, peak = 89), gca=88.6
gca peak = 0.08071 (86)
mri peak = 0.13502 (83)
Left_Putamen (12): linear fit = 1.00 x + 0.0 (1573 voxels, overlap=0.728)
Left_Putamen (12): linear fit = 1.00 x + 0.0 (1573 voxels, peak = 86), gca=86.0
gca peak = 0.08515 (83)
mri peak = 0.11726 (83)
Right_Putamen (51): linear fit = 0.98 x + 0.0 (1825 voxels, overlap=0.899)
Right_Putamen (51): linear fit = 0.98 x + 0.0 (1825 voxels, peak = 81), gca=80.9
gca peak = 0.08056 (89)
mri peak = 0.10605 (90)
Brain_Stem (16): linear fit = 0.98 x + 0.0 (10876 voxels, overlap=0.681)
Brain_Stem (16): linear fit = 0.98 x + 0.0 (10876 voxels, peak = 87), gca=86.8
gca peak = 0.11663 (97)
mri peak = 0.13885 (100)
Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1312 voxels, overlap=0.746)
Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1312 voxels, peak = 97), gca=97.5
gca peak = 0.14874 (96)
mri peak = 0.16600 (100)
Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1302 voxels, overlap=0.803)
Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1302 voxels, peak = 96), gca=95.5
gca peak = 0.16499 (28)
mri peak = 0.33333 (21)
gca peak = 0.16433 (21)
mri peak = 0.28004 (20)
Fourth_Ventricle (15): linear fit = 0.99 x + 0.0 (117 voxels, overlap=0.741)
Fourth_Ventricle (15): linear fit = 0.99 x + 0.0 (117 voxels, peak = 21), gca=20.7
gca peak Unknown = 0.94777 ( 0)
gca peak Left_Inf_Lat_Vent = 0.19147 (31)
gca peak Third_Ventricle = 0.16499 (28)
gca peak CSF = 0.18962 (39)
gca peak Left_Accumbens_area = 0.54460 (62)
gca peak Left_undetermined = 1.00000 (28)
gca peak Left_vessel = 0.89917 (53)
gca peak Left_choroid_plexus = 0.11541 (35)
gca peak Right_Inf_Lat_Vent = 0.26261 (28)
gca peak Right_Accumbens_area = 0.30638 (73)
gca peak Right_vessel = 0.77268 (52)
gca peak Right_choroid_plexus = 0.13275 (38)
gca peak Fifth_Ventricle = 0.73548 (35)
gca peak WM_hypointensities = 0.11093 (77)
gca peak non_WM_hypointensities = 0.14635 (41)
gca peak Optic_Chiasm = 0.51659 (76)
not using caudate to estimate GM means
estimating mean gm scale to be 1.00 x + 0.0
estimating mean wm scale to be 1.00 x + 0.0
estimating mean csf scale to be 1.00 x + 0.0
saving intensity scales to aseg.auto_noCCseg.label_intensities.txt
saving sequentially combined intensity scales to aseg.auto_noCCseg.label_intensities.txt
33606 voxels changed in iteration 0 of unlikely voxel relabeling
154 voxels changed in iteration 1 of unlikely voxel relabeling
5 voxels changed in iteration 2 of unlikely voxel relabeling
0 voxels changed in iteration 3 of unlikely voxel relabeling
28971 gm and wm labels changed (%38 to gray, %62 to white out of all changed labels)
457 hippocampal voxels changed.
0 amygdala voxels changed.
Reclassifying using Gibbs Priors
pass 1: 47706 changed. image ll: -2.036, PF=0.500
pass 2: 11160 changed. image ll: -2.036, PF=0.500
pass 3: 3295 changed.
23625 voxels changed in iteration 0 of unlikely voxel relabeling
220 voxels changed in iteration 1 of unlikely voxel relabeling
18 voxels changed in iteration 2 of unlikely voxel relabeling
0 voxels changed in iteration 3 of unlikely voxel relabeling
5364 voxels changed in iteration 0 of unlikely voxel relabeling
137 voxels changed in iteration 1 of unlikely voxel relabeling
9 voxels changed in iteration 2 of unlikely voxel relabeling
1 voxels changed in iteration 3 of unlikely voxel relabeling
0 voxels changed in iteration 4 of unlikely voxel relabeling
4516 voxels changed in iteration 0 of unlikely voxel relabeling
83 voxels changed in iteration 1 of unlikely voxel relabeling
0 voxels changed in iteration 2 of unlikely voxel relabeling
4023 voxels changed in iteration 0 of unlikely voxel relabeling
26 voxels changed in iteration 1 of unlikely voxel relabeling
2 voxels changed in iteration 2 of unlikely voxel relabeling
1 voxels changed in iteration 3 of unlikely voxel relabeling
0 voxels changed in iteration 4 of unlikely voxel relabeling
!!!!!!!!! ventricle segment 0 with volume 5074 above threshold 100 - not erasing !!!!!!!!!!
!!!!!!!!! ventricle segment 1 with volume 6332 above threshold 100 - not erasing !!!!!!!!!!
writing labeled volume to aseg.auto_noCCseg.mgz
mri_ca_label utimesec 2357.275314
mri_ca_label stimesec 15.937804
mri_ca_label ru_maxrss 2129660
mri_ca_label ru_ixrss 0
mri_ca_label ru_idrss 0
mri_ca_label ru_isrss 0
mri_ca_label ru_minflt 11280482
mri_ca_label ru_majflt 0
mri_ca_label ru_nswap 0
mri_ca_label ru_inblock 0
mri_ca_label ru_oublock 608
mri_ca_label ru_msgsnd 0
mri_ca_label ru_msgrcv 0
mri_ca_label ru_nsignals 0
mri_ca_label ru_nvcsw 635
mri_ca_label ru_nivcsw 5726
auto-labeling took 35 minutes and 7 seconds.
#--------------------------------------
#@# CC Seg Mon Feb 5 03:14:39 UTC 2024
mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /out/sourcedata/freesurfer/sub-CH8698b/mri/transforms/cc_up.lta sub-CH8698b
will read input aseg from aseg.auto_noCCseg.mgz
writing aseg with cc labels to aseg.auto.mgz
will write lta as /out/sourcedata/freesurfer/sub-CH8698b/mri/transforms/cc_up.lta
reading aseg from /out/sourcedata/freesurfer/sub-CH8698b/mri/aseg.auto_noCCseg.mgz
reading norm from /out/sourcedata/freesurfer/sub-CH8698b/mri/norm.mgz
29359 voxels in left wm, 30623 in right wm, xrange [125, 132]
searching rotation angles z=[-6 8], y=[-8 6]
searching scale 1 Z rot -6.0
searching scale 1 Z rot -5.8
searching scale 1 Z rot -5.5
searching scale 1 Z rot -5.3
searching scale 1 Z rot -5.0
searching scale 1 Z rot -4.8
searching scale 1 Z rot -4.5
searching scale 1 Z rot -4.3
searching scale 1 Z rot -4.0
searching scale 1 Z rot -3.8
searching scale 1 Z rot -3.5
searching scale 1 Z rot -3.3
searching scale 1 Z rot -3.0
searching scale 1 Z rot -2.8
searching scale 1 Z rot -2.5
searching scale 1 Z rot -2.3
searching scale 1 Z rot -2.0
searching scale 1 Z rot -1.8
searching scale 1 Z rot -1.5
searching scale 1 Z rot -1.3
searching scale 1 Z rot -1.0
searching scale 1 Z rot -0.8
searching scale 1 Z rot -0.5
searching scale 1 Z rot -0.3
searching scale 1 Z rot -0.0
searching scale 1 Z rot 0.2
searching scale 1 Z rot 0.5
searching scale 1 Z rot 0.7
searching scale 1 Z rot 1.0
searching scale 1 Z rot 1.2
searching scale 1 Z rot 1.5
searching scale 1 Z rot 1.7
searching scale 1 Z rot 2.0
searching scale 1 Z rot 2.2
searching scale 1 Z rot 2.5
searching scale 1 Z rot 2.7
searching scale 1 Z rot 3.0
searching scale 1 Z rot 3.2
searching scale 1 Z rot 3.5
searching scale 1 Z rot 3.7
searching scale 1 Z rot 4.0
searching scale 1 Z rot 4.2
searching scale 1 Z rot 4.5
searching scale 1 Z rot 4.7
searching scale 1 Z rot 5.0
searching scale 1 Z rot 5.2
searching scale 1 Z rot 5.5
searching scale 1 Z rot 5.7
searching scale 1 Z rot 6.0
searching scale 1 Z rot 6.2
searching scale 1 Z rot 6.5
searching scale 1 Z rot 6.7
searching scale 1 Z rot 7.0
searching scale 1 Z rot 7.2
searching scale 1 Z rot 7.5
searching scale 1 Z rot 7.7 global minimum found at slice 128.0, rotations (-1.12, 0.99)
final transformation (x=128.0, yr=-1.118, zr=0.994):
0.99966 -0.01734 -0.01951 3.63136;
0.01734 0.99985 -0.00034 43.83077;
0.01952 0.00000 0.99981 14.52321;
0.00000 0.00000 0.00000 1.00000;
updating x range to be [126, 131] in xformed coordinates
best xformed slice 128
min_x_fornix = 132
min_x_fornix = 135
WARNING: min_x_fornix not set
min_x_fornix = 139
min_x_fornix = 139
cc center is found at 128 82 111
eigenvectors:
-0.00040 0.00302 1.00000;
0.15001 -0.98868 0.00305;
0.98868 0.15001 -0.00005;
writing aseg with callosum to /out/sourcedata/freesurfer/sub-CH8698b/mri/aseg.auto.mgz...
corpus callosum segmentation took 0.6 minutes
#VMPC# mri_cc VmPeak 947804
mri_cc done
#--------------------------------------
#@# Merge ASeg Mon Feb 5 03:15:15 UTC 2024
cp aseg.auto.mgz aseg.presurf.mgz
#--------------------------------------------
#@# Intensity Normalization2 Mon Feb 5 03:15:15 UTC 2024
/out/sourcedata/freesurfer/sub-CH8698b/mri
mri_normalize -seed 1234 -mprage -aseg aseg.presurf.mgz -mask brainmask.mgz norm.mgz brain.mgz
setting seed for random number genererator to 1234
assuming input volume is MGH (Van der Kouwe) MP-RAGE
using segmentation for initial intensity normalization
using MR volume brainmask.mgz to mask input volume...
reading mri_src from norm.mgz...
Reading aseg aseg.presurf.mgz
normalizing image...
NOT doing gentle normalization with control points/label
processing with aseg
removing outliers in the aseg WM...
326 control points removed
Building bias image
building Voronoi diagram...
performing soap bubble smoothing, sigma = 0...
Smoothing with sigma 8
Applying bias correction
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
Iterating 2 times
---------------------------------
3d normalization pass 1 of 2
white matter peak found at 110
white matter peak found at 110
gm peak at 72 (72), valley at 59 (59)
csf peak at 37, setting threshold to 60
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
---------------------------------
3d normalization pass 2 of 2
white matter peak found at 110
white matter peak found at 110
gm peak at 74 (74), valley at 33 (33)
csf peak at 38, setting threshold to 62
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
Done iterating ---------------------------------
writing output to brain.mgz
3D bias adjustment took 2 minutes and 33 seconds.
#--------------------------------------------
#@# Mask BFS Mon Feb 5 03:17:50 UTC 2024
/out/sourcedata/freesurfer/sub-CH8698b/mri
mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz
threshold mask volume at 5
DoAbs = 0
Found 1219769 voxels in mask (pct= 7.27)
Writing masked volume to brain.finalsurfs.mgz...done.
#--------------------------------------------
#@# WM Segmentation Mon Feb 5 03:17:51 UTC 2024
AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz
mri_segment -wsizemm 13 -mprage antsdn.brain.mgz wm.seg.mgz
wsizemm = 13, voxres = 1, wsize = 13
Widening wm low from 89 to 79
assuming input volume is MGH (Van der Kouwe) MP-RAGE
wm mean: 110
wsize: 13
wm low: 79
wm hi: 125
gray low: 30
gray hi: 99
Doing initial trinary intensity segmentation
Using local statistics to label ambiguous voxels
Autodetecting stats
Computing class statistics for intensity windows...
CCS WM (102.0): 101.4 +- 6.4 [79.0 --> 118.0]
CCS GM (76.0) : 75.2 +- 9.4 [30.0 --> 95.0]
white_mean 101.448
white_sigma 6.42697
gray_mean 75.1583
gray_sigma 9.41918
setting bottom of white matter range wm_low to 84.6
setting top of gray matter range gray_hi to 94.0
wm_low 84.5775
wm_hi 125
gray_low 30
gray_hi 93.9967
Redoing initial intensity segmentation...
Recomputing local statistics to label ambiguous voxels...
wm_low 84.5775
wm_hi 125
gray_low 30
gray_hi 93.9967
using local geometry to label remaining ambiguous voxels...
polvwsize = 5, polvlen = 3, gray_hi = 93.9967, wm_low = 84.5775
MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=93.9967, wmlow=84.5775
103321 voxels processed (0.62%)
43542 voxels white (0.26%)
59779 voxels non-white (0.36%)
Reclassifying voxels using Gaussian border classifier niter=1
MRIreclassify(): wm_low=79.5775, gray_hi=93.9967, wsize=13
204895 voxels tested (1.22%)
37349 voxels changed (0.22%)
48274 multi-scale searches (0.29%)
Recovering bright white
MRIrecoverBrightWhite()
wm_low 84.5775
wm_hi 125
slack 6.42697
pct_thresh 0.33
intensity_thresh 131.427
nvox_thresh 8.58
0 voxels tested (0.00%)
0 voxels changed (0.00%)
removing voxels with positive offset direction...
MRIremoveWrongDirection() wsize=3, lowthr=79.5775, hithr=93.9967
smoothing input volume with sigma = 0.250
62843 voxels tested (0.37%)
12460 voxels changed (0.07%)
thicken = 1
removing 1-dimensional structures...
MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5
1194 sparsely connected voxels removed in 1 iterations
thickening thin strands....
thickness 4
nsegments 20
wm_hi 125
767 diagonally connected voxels added...
MRIthickenThinWMStrands(): thickness=4, nsegments=20
20 segments, 4179 filled
MRIfindBrightNonWM(): 0 bright non-wm voxels segmented.
MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230
white matter segmentation took 1.3 minutes
writing output to wm.seg.mgz...
mri_edit_wm_with_aseg -keep-in wm.seg.mgz brain.mgz aseg.presurf.mgz wm.asegedit.mgz
preserving editing changes in input volume...
auto filling took 0.55 minutes
reading wm segmentation from wm.seg.mgz...
0 voxels added to wm to prevent paths from MTL structures to cortex
2743 additional wm voxels added
0 additional wm voxels added
SEG EDIT: 35218 voxels turned on, 36481 voxels turned off.
propagating editing to output volume from wm.seg.mgz
writing edited volume to wm.asegedit.mgz....
mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz
Iteration Number : 1
pass 1 (xy+): 4 found - 4 modified | TOTAL: 4
pass 2 (xy+): 0 found - 4 modified | TOTAL: 4
pass 1 (xy-): 11 found - 11 modified | TOTAL: 15
pass 2 (xy-): 0 found - 11 modified | TOTAL: 15
pass 1 (yz+): 20 found - 20 modified | TOTAL: 35
pass 2 (yz+): 0 found - 20 modified | TOTAL: 35
pass 1 (yz-): 11 found - 11 modified | TOTAL: 46
pass 2 (yz-): 0 found - 11 modified | TOTAL: 46
pass 1 (xz+): 7 found - 7 modified | TOTAL: 53
pass 2 (xz+): 0 found - 7 modified | TOTAL: 53
pass 1 (xz-): 9 found - 9 modified | TOTAL: 62
pass 2 (xz-): 0 found - 9 modified | TOTAL: 62
Iteration Number : 1
pass 1 (+++): 4 found - 4 modified | TOTAL: 4
pass 2 (+++): 0 found - 4 modified | TOTAL: 4
pass 1 (+++): 0 found - 0 modified | TOTAL: 4
pass 1 (+++): 0 found - 0 modified | TOTAL: 4
pass 1 (+++): 2 found - 2 modified | TOTAL: 6
pass 2 (+++): 0 found - 2 modified | TOTAL: 6
Iteration Number : 1
pass 1 (++): 21 found - 21 modified | TOTAL: 21
pass 2 (++): 0 found - 21 modified | TOTAL: 21
pass 1 (+-): 35 found - 35 modified | TOTAL: 56
pass 2 (+-): 0 found - 35 modified | TOTAL: 56
pass 1 (--): 13 found - 13 modified | TOTAL: 69
pass 2 (--): 0 found - 13 modified | TOTAL: 69
pass 1 (-+): 43 found - 43 modified | TOTAL: 112
pass 2 (-+): 0 found - 43 modified | TOTAL: 112
Iteration Number : 2
pass 1 (xy+): 1 found - 1 modified | TOTAL: 1
pass 2 (xy+): 0 found - 1 modified | TOTAL: 1
pass 1 (xy-): 1 found - 1 modified | TOTAL: 2
pass 2 (xy-): 0 found - 1 modified | TOTAL: 2
pass 1 (yz+): 0 found - 0 modified | TOTAL: 2
pass 1 (yz-): 0 found - 0 modified | TOTAL: 2
pass 1 (xz+): 1 found - 1 modified | TOTAL: 3
pass 2 (xz+): 0 found - 1 modified | TOTAL: 3
pass 1 (xz-): 0 found - 0 modified | TOTAL: 3
Iteration Number : 2
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
Iteration Number : 2
pass 1 (++): 0 found - 0 modified | TOTAL: 0
pass 1 (+-): 0 found - 0 modified | TOTAL: 0
pass 1 (--): 0 found - 0 modified | TOTAL: 0
pass 1 (-+): 0 found - 0 modified | TOTAL: 0
Iteration Number : 3
pass 1 (xy+): 0 found - 0 modified | TOTAL: 0
pass 1 (xy-): 0 found - 0 modified | TOTAL: 0
pass 1 (yz+): 0 found - 0 modified | TOTAL: 0
pass 1 (yz-): 0 found - 0 modified | TOTAL: 0
pass 1 (xz+): 0 found - 0 modified | TOTAL: 0
pass 1 (xz-): 0 found - 0 modified | TOTAL: 0
Iteration Number : 3
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
pass 1 (+++): 0 found - 0 modified | TOTAL: 0
Iteration Number : 3
pass 1 (++): 0 found - 0 modified | TOTAL: 0
pass 1 (+-): 0 found - 0 modified | TOTAL: 0
pass 1 (--): 0 found - 0 modified | TOTAL: 0
pass 1 (-+): 0 found - 0 modified | TOTAL: 0
Total Number of Modified Voxels = 183 (out of 465866: 0.039282)
binarizing input wm segmentation...
Ambiguous edge configurations...
mri_pretess done
#--------------------------------------------
#@# Fill Mon Feb 5 03:20:12 UTC 2024
/out/sourcedata/freesurfer/sub-CH8698b/mri
mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz -ctab /opt/freesurfer/SubCorticalMassLUT.txt wm.mgz filled.mgz
logging cutting plane coordinates to ../scripts/ponscc.cut.log...
INFO: Using transforms/talairach.lta and its offset for Talairach volume ...
using segmentation aseg.presurf.mgz...
reading input volume...done.
searching for cutting planes...voxel to talairach voxel transform
1.16735 -0.03830 -0.00903 -19.57378;
0.04113 1.23200 0.05566 -14.05557;
0.00973 -0.05271 1.03939 -3.43275;
0.00000 0.00000 0.00000 1.00000;
voxel to talairach voxel transform
1.16735 -0.03830 -0.00903 -19.57378;
0.04113 1.23200 0.05566 -14.05557;
0.00973 -0.05271 1.03939 -3.43275;
0.00000 0.00000 0.00000 1.00000;
reading segmented volume aseg.presurf.mgz
removing CC from segmentation
Looking for area (min, max) = (350, 1400)
area[0] = 2369 (min = 350, max = 1400), aspect = 1.32 (min = 0.10, max = 0.75)
need search nearby
using seed (126, 123, 151), TAL = (2.0, 23.0, 5.0)
talairach voxel to voxel transform
0.85565 0.02685 0.00599 17.14623;
-0.02814 0.80895 -0.04357 10.66991;
-0.00943 0.04077 0.95983 3.68335;
0.00000 0.00000 0.00000 1.00000;
segmentation indicates cc at (126, 123, 151) --> (2.0, 23.0, 5.0)
done.
filling took 1.0 minutes
talairach cc position changed to (2.00, 23.00, 5.00)
Erasing brainstem...done.
seed_search_size = 9, min_neighbors = 5
search rh wm seed point around talairach space:(20.00, 23.00, 5.00) SRC: (113.76, 100.55, 152.61)
search lh wm seed point around talairach space (-16.00, 23.00, 5.00), SRC: (144.57, 99.54, 152.28)
compute mri_fill using aseg
Erasing Brain Stem and Cerebellum ...
Define left and right masks using aseg:
Building Voronoi diagram ...
Using the Voronoi diagram for separating WM into two hemispheres ...
Find the largest connected component for each hemisphere ...
Embedding colortable
mri_fill done, writing output to filled.mgz...
cp filled.mgz filled.auto.mgz
cp: skipping file 'filled.mgz', as it was replaced while being copied
Linux a6080e14df56 5.4.0-169-generic #187-Ubuntu SMP Thu Nov 23 14:52:28 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
recon-all -s sub-CH8698b exited with ERRORS at Mon Feb 5 03:21:14 UTC 2024
For more details, see the log file /out/sourcedata/freesurfer/sub-CH8698b/scripts/recon-all.log
To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
Stderr:
cat: standard output: Stale file handle
cat: standard output: Stale file handle
Traceback:
RuntimeError: subprocess exited with code 1.
Copy or symlink failures make me think it's probably a filesystem capabilities issue. What type of filesystem is /Volumes/NMorphCHderivatives
? If it's a FAT/NTFS filesystem, it's possible that you're hitting some issues with a non-POSIX FS appearing to the process as POSIX.
Your suggestion is making perfect sense, the data lives in a windows/cifs/samba NAS.
I have taken a copy out to the local filesystem (ext4), and so far fmriprep has perfectly preprocessed 4 subjects without errors. I will come back tomorrow with a higher sample, hopefully just to close the bug report.
Thank you.
What happened?
We are running fmriprep within docker as non-root users for a pretty standard subject database (rs-fMRI, T1, T2). It's working great except for the fact that each user takes 2 or 3 attempts of the same command and cleaning-up of the failed derivatives folder before success.
We think we have ruled out concurrency issues. Even if run serially, freesurfer sometimes fails and sometimes doesn't for exactly the same data and invocation (and it would be great to be able to process many users in parallel anyway, but first we want to figure out how to increase our success rate).
Are we doing something wrong? The logs are a bit arcane to us.
What command did you use?
What version of fMRIPrep are you running?
fMRIPrep v23.2.0
How are you running fMRIPrep?
Docker
Is your data BIDS valid?
Yes
Are you reusing any previously computed results?
No
Please copy and paste any relevant log output.
Additional information / screenshots
sourcedata/freesurfer/sub-CH8461a/scripts/recon-all-rh.log seems to contain the same data as the previous log.