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== Process Flow ==
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||<rowbgcolor='#80FF80'>'''recon-all step'''||'''Input'''||'''Command Line'''||'''Output'''||
||<|10(bgcolor='#FFFFE0'>["recon-all"] -autorecon1 -subjid subj -no_edit_wm_with_aseg||
||<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 -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||

[wiki:FreeSurferWorkFlows top] | [wiki:HistoricalReconstruction previous]

FreeSurfer Subcortical Segmentation

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:

  • 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

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.

recon-all -subjid <subject name> -autorecon1 -noedit_wm_with_aseg

Process Flow

recon-all step

Input

Command Line

Output

["recon-all"] -autorecon1 -subjid subj -no_edit_wm_with_aseg

orig/001.mgz

["mri_motion_correct2"] -i orig/001.mgz -i orig/002.mgz -o rawavg.mgz

rawavg.mgz

orig/002.mgz

rawavg.mgz

["mri_convert"] rawavg.mgz orig.mgz --conform

orig.mgz

orig.mgz

["mri_convert"] orig.mgz orig.mnc

orig.mnc

orig.mnc

(4 iterations of) ["nu_correct"] -clobber nu0.mnc nu1.mnc

nu4.mnc

nu4.mnc

["mri_convert"] nu4.mnc nu.mgz

nu.mgz

nu.mgz

["talairach2"] subjid -mgz

transforms/talairach.xfm

nu.mgz

["mri_normalize"] nu.mgz T1.mgz

T1.mgz

T1.mgz

["mri_watershed"] T1.mgz brain.mgz

brain.mgz

Check skullstrip (brain.mgz), talairach (transforms/talairach.xfm), and normalization (brain.mgz or T1.mgz - mean wm voxel value = 110)

["recon-all"] -autorecon2 -subjid subj

brain.mgz

["mri_em_register"] -mask brain.mgz -p .5 -fsamples fsamples.mgz nu.mgz $GCA transforms/talairach.lta

transforms/talairach.lta

nu.mgz

brain.mgz

["mri_ca_normalize"] -mask brain.mgz nu.mgz $GCA transforms/talairach.lta norm.mgz

norm.mgz

nu.mgz

transforms/talairach.lta

brain.mgz

["mri_ca_register"] -cross-sequence -mask brain.mgz -T transforms/talairach.lta norm.mgz $GCA transforms/talairach.m3z

transforms/talairach.m3z

transforms/talairach.lta

norm.mgz

norm.mgz

["mri_ca_label"] -cross-sequence norm.mgz transforms/talairach.m3z $GCA aseg.mgz

aseg.mgz

transforms/talairach.m3z

brain.mgz

["mri_normalize"] -mask brain.mgz nu.mgz T1.mgz

T1.mgz

nu.mgz

T1.mgz

["mri_mask"] T1.mgz brain.mgz brain.mgz

brain.mgz

brain.mgz

brain.mgz

["mri_segment"] brain.mgz wm.mgz

wm.mgz

wm.mgz

["mri_edit_wm_with_aseg"] wm.mgz aseg.mgz wm.mgz

wm.mgz

aseg.mgz

SubcorticalSegmentation (last edited 2018-01-04 15:07:58 by MorganFogarty)