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| FreeSurfer now automatically runs automated labeling of the brain volume and this is included in all versions of the September 2005 release. In subcortical segmentation, each voxel in the normalized brain volume is assigned one of about 40 labels, including: | In automatical subcortical segmentation, each voxel in the normalized brain volume is assigned one of about 40 labels, including: |
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| However, if you processed your anatomical data using previous versions, you can just run the subcortical segmentation separately if you wish to obtain the automated labels. The September 2005 release of FreeSurfer by default uses the automatically segmented brain volume (ASEG) to segment the white matter volume (WM). You will use the -noedit_wm_with_aseg flag to ensure that it preserves and uses the white matter volume (WM) that you edited. | FreeSurfer now runs automated labeling of the brain volume and this is included in all versions of the January 2006 release. However, if you processed your anatomical data using previous versions and you wish to obtain the automated labels, you can just run the subcortical segmentation separately. It is important to note that the January 2006 release of FreeSurfer by default uses the automatically segmented brain volume (ASEG - in mri/aseg.mgz) to segment the white matter volume (WM - mri/wm). You must therefore use the -noedit_wm_with_aseg flag to ensure that it preserves and uses the white matter volume (WM) edits that you made, ''if'' you rerun any later steps on your previously edited data -- e.g. if for some reason you wish to regenerate final surfaces. So, to obtain automatically segmented volumes for the first time, run: {{{ recon-all -subcortseg -segstats -subjid <subject name> }}} |
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| The automatic subcortical segmentation can take many (16+) hours to complete. To view the segmentation, use this command: |
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| recon-all -autorecon2 -noedit_wm_with_aseg -keepwmedits -subjid <subject name> | tkmedit <subject name> norm.mgz -segmentation mri/aseg.mgz $FREESURFER_HOME/tkmeditColorsCMA |
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| == Process Flow == ||<rowbgcolor='#80FF80'>'''recon-all step'''||'''Input'''||'''Command Line'''||'''Output'''|| ||<|10(bgcolor='#FFFFE0'>["recon-all"] -autorecon1 -subjid subj|| ||<rowbgcolor='#E0E0FF'>orig/001.mgz||<|2(>["mri_motion_correct2"] -i orig/001.mgz -i orig/002.mgz -o rawavg.mgz||<|2(>rawavg.mgz|| ||<rowbgcolor='#E0E0FF'>orig/002.mgz|| ||<rowbgcolor='#E0E0FF'>rawavg.mgz||["mri_convert"] rawavg.mgz orig.mgz --conform||orig.mgz|| ||<rowbgcolor='#E0E0FF'>orig.mgz||["mri_convert"] orig.mgz orig.mnc||orig.mnc|| ||<rowbgcolor='#E0E0FF'>orig.mnc||(4 iterations of) ["nu_correct"] -clobber nu0.mnc nu1.mnc||nu4.mnc|| ||<rowbgcolor='#E0E0FF'>nu4.mnc||["mri_convert"] nu4.mnc nu.mgz||nu.mgz|| ||<rowbgcolor='#E0E0FF'>nu.mgz||["talairach2"] subjid -mgz||transforms/talairach.xfm|| ||<rowbgcolor='#E0E0FF'>nu.mgz||["mri_normalize"] nu.mgz T1.mgz||T1.mgz|| ||<rowbgcolor='#E0E0FF'>T1.mgz||["mri_watershed"] T1.mgz brain.mgz||brain.mgz|| ||<-4(rowbgcolor='#FF8080'>Check skullstrip (brain.mgz), talairach (transforms/talairach.xfm), and normalization (brain.mgz or T1.mgz - mean wm voxel value = 110)|| ||<|35(bgcolor='#FFFFE0'>["recon-all"] -autorecon2 -no_edit_wm_with_aseg -keepwmedits -subjid subj|| ||<rowbgcolor='#E0E0FF'>brain.mgz||<|2(>["mri_em_register"] -mask brain.mgz -p .5 -fsamples fsamples.mgz nu.mgz $GCA transforms/talairach.lta||<|2(>transforms/talairach.lta|| ||<rowbgcolor='#E0E0FF'>nu.mgz|| ||<rowbgcolor='#E0E0FF'>brain.mgz||<|3(>["mri_ca_normalize"] -mask brain.mgz nu.mgz $GCA transforms/talairach.lta norm.mgz||<|3(>norm.mgz|| ||<rowbgcolor='#E0E0FF'>nu.mgz|| ||<rowbgcolor='#E0E0FF'>transforms/talairach.lta|| ||<rowbgcolor='#E0E0FF'>brain.mgz||<|3(>["mri_ca_register"] -cross-sequence -mask brain.mgz -T transforms/talairach.lta norm.mgz $GCA transforms/talairach.m3z||<|3(>transforms/talairach.m3z|| ||<rowbgcolor='#E0E0FF'>transforms/talairach.lta|| ||<rowbgcolor='#E0E0FF'>norm.mgz|| ||<rowbgcolor='#E0E0FF'>norm.mgz||<|2(>["mri_ca_label"] -cross-sequence norm.mgz transforms/talairach.m3z $GCA aseg.mgz||<|2(>aseg.mgz|| ||<rowbgcolor='#E0E0FF'>transforms/talairach.m3z|| ||<rowbgcolor='#E0E0FF'>brain.mgz||<|2(>["mri_normalize"] -mask brain.mgz nu.mgz T1.mgz||<|2(>T1.mgz|| ||<rowbgcolor='#E0E0FF'>nu.mgz|| ||<rowbgcolor='#E0E0FF'>T1.mgz||<|2(>["mri_mask"] T1.mgz brain.mgz brain.mgz||<|2(>brain.mgz|| ||<rowbgcolor='#E0E0FF'>brain.mgz|| ||<rowbgcolor='#E0E0FF'>brain.mgz||["mri_segment"] brain.mgz wm.mgz||wm.mgz|| ||<rowbgcolor='#E0E0FF'>wm.mgz||<|2(>["mri_edit_wm_with_aseg"] wm.mgz aseg.mgz wm.mgz||<|2(>wm.mgz|| ||<rowbgcolor='#E0E0FF'>aseg.mgz|| |
Float your cursor over any voxel and the label assigned to it will be displayed in the TkMeditTools window. |
[wiki:FreeSurferWorkFlows top] | [wiki:HistoricalReconstruction previous]
FreeSurfer Subcortical Segmentation
In automatical subcortical segmentation, each voxel in the normalized brain volume is assigned one of about 40 labels, including:
- Cerebral White Matter, Cerebral Cortex, Lateral Ventricle, Inferior Lateral Ventricle, Cerebellum White Matter, Cerebellum Cortex, Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, Lesion, Accumbens area, Vessel, Central Diencephalon, Third Ventricle, Fourth Ventricle, Brain Stem, Cerebrospinal Fluid
FreeSurfer now runs automated labeling of the brain volume and this is included in all versions of the January 2006 release. However, if you processed your anatomical data using previous versions and you wish to obtain the automated labels, you can just run the subcortical segmentation separately.
It is important to note that the January 2006 release of FreeSurfer by default uses the automatically segmented brain volume (ASEG - in mri/aseg.mgz) to segment the white matter volume (WM - mri/wm). You must therefore use the -noedit_wm_with_aseg flag to ensure that it preserves and uses the white matter volume (WM) edits that you made, if you rerun any later steps on your previously edited data -- e.g. if for some reason you wish to regenerate final surfaces.
So, to obtain automatically segmented volumes for the first time, run:
recon-all -subcortseg -segstats -subjid <subject name>
The automatic subcortical segmentation can take many (16+) hours to complete.
To view the segmentation, use this command:
tkmedit <subject name> norm.mgz -segmentation mri/aseg.mgz $FREESURFER_HOME/tkmeditColorsCMA
Float your cursor over any voxel and the label assigned to it will be displayed in the TkMeditTools window.