Output files

Segmentation module

The segmentation module outputs the files shown in the table below. The two primary output files are the aparc.DKTatlas+aseg.deep.mgz file, which contains the FastSurfer segmentation of cortical and subcortical structures based on the DKT atlas, and the aseg+DKT.stats file, which contains summary statistics for these structures. Note, that the surface model (downstream) corrects these segmentations along the cortex with the created surfaces. So if the surface model is used, it is recommended to use the updated segmentations and stats (see below).

directory	filename	module	description
mri	aparc.DKTatlas+aseg.deep.mgz	asegdkt	cortical and subcortical segmentation
mri	aseg.auto_noCCseg.mgz	asegdkt	simplified subcortical segmentation without corpus callosum labels
mri	mask.mgz	asegdkt	brainmask
mri	orig.mgz	asegdkt	conformed image
mri	orig_nu.mgz	asegdkt	biasfield-corrected image
mri/orig	001.mgz	asegdkt	original image
scripts	deep-seg.log	asegdkt	logfile
stats	aseg+DKT.stats	asegdkt	table of cortical and subcortical segmentation statistics

Corpus Callosum module

The Corpus Callosum module outputs the files in the table shown below. It creates detailed segmentations and shape analysis of the corpus callosum. For advanced output refer to the FastSurfer-CC documentation.

directory	filename	module	description
mri	callosum.CC.upright.mgz	cc	corpus callosum segmentation in upright space
mri	callosum.CC.orig.mgz	cc	corpus callosum segmentation in conformed image orientation
mri	callosum.CC.soft.mgz	cc	corpus callosum soft labels (in upright space)
mri	fornix.CC.soft.mgz	cc	fornix soft labels (in upright space)
mri	background.CC.soft.mgz	cc	background soft labels (in upright space)
mri	upright_volume.mgz	cc	conformed image mapped to upright space (only with fastsurfer_cc.py --upright_volume)
mri/transforms	cc_up.lta	cc	transform from conformed to upright space
mri/transforms	orient_volume.lta	cc	transform to standardized space
stats	callosum.CC.midslice.json	cc	measurements from the mid-sagittal slice (landmarks, area, thickness, etc.)
stats	callosum.CC.all_slices.json	cc	comprehensive per-slice analysis
qc_snapshots	callosum.png	cc	debug visualization of CC contours, AC, PC and thickness (only with run_fastsurfer.sh --qc_snap)
qc_snapshots	callosum_thickness.png	cc	3D thickness visualization (only with run_fastsurfer.sh --qc_snap)
qc_snapshots	corpus_callosum.html	cc	interactive 3D mesh visualization (only with run_fastsurfer.sh --qc_snap)
surf	callosum.surf	cc	3D Corpus Callosum mesh in FreeSurfer surface format (open with freeview)
surf	callosum.thickness.w	cc	FreeSurfer overlay file containing thickness values (open with callosum.surf in freeview)
surf	callosum.vtk	cc	VTK format mesh file for 3D visualization

CerebNet module

The cerebellum module outputs the files in the table shown below. Unless switched off by the --no_cereb argument, this module is automatically run whenever the segmentation module is run. It adds two files, an image with the sub-segmentation of the cerebellum and a text file with summary statistics.

directory	filename	module	description
mri	cerebellum.CerebNet.nii.gz	cerebnet	cerebellum sub-segmentation
stats	cerebellum.CerebNet.stats	cerebnet	table of cerebellum segmentation statistics

HypVINN module

The hypothalamus module outputs the files in the table shown below. Unless switched off by the --no_hypothal argument, this module is automatically run whenever the segmentation module is run. It adds three files, an image with the sub-segmentation of the hypothalamus and a text file with summary statistics.

directory	filename	module	description
mri	hypothalamus.HypVINN.nii.gz	hypvinn	hypothalamus sub-segmentation
mri	hypothalamus_mask.HypVINN.nii.gz	hypvinn	hypothalamus sub-segmentation mask
stats	hypothalamus.HypVINN.stats	hypvinn	table of hypothalamus segmentation statistics

If a T2 image is also passed, the following images are created.

directory	filename	module	description
mri	T2_nu.mgz	hypvinn	biasfield-corrected T2 image
mri	T2_nu_reg.mgz	hypvinn	co-registered T2 to orig image

Surface module

The surface module is run unless switched off by the --seg_only argument. It outputs a large number of files, which generally correspond to the FreeSurfer nomenclature and definition. A selection of important output files is shown in the table below, for the other files, we refer to the FreeSurfer documentation. In general, the “mri” directory contains images, including segmentations, the “surf” folder contains surface files (geometries and vertex-wise overlay data), the “label” folder contains cortical parcellation labels, and the “stats” folder contains tabular summary statistics. Many files are available for the left (“lh”) and right (“rh”) hemisphere of the brain. Symbolic links are created to map FastSurfer files to their FreeSurfer equivalents, which may need to be present for further processing (e.g., with FreeSurfer downstream modules).

After running this module, some of the initial segmentations and corresponding volume estimates are fine-tuned (e.g., surface-based partial volume correction, addition of corpus callosum labels). Specifically, this concerns the aseg.mgz , aparc.DKTatlas+aseg.mapped.mgz, aparc.DKTatlas+aseg.deep.withCC.mgz, which were originally created by the segmentation module or have earlier versions resulting from that module.

The primary output files are pial, white, and inflated surface files, the thickness overlay files, and the cortical parcellation (annotation) files. The preferred way of assessing this output is the FreeView software. Summary statistics for volume and thickness estimates per anatomical structure are reported in the stats files, in particular the aseg.stats, and the left and right aparc.DKTatlas.mapped.stats files.

directory	filename	module	description
mri	aparc.DKTatlas+aseg.deep.withCC.mgz	surface	cortical and subcortical segmentation incl. corpus callosum after running the surface module
mri	aparc.DKTatlas+aseg.mapped.mgz	surface	cortical and subcortical segmentation after running the surface module
mri	aparc.DKTatlas+aseg.mgz	surface	symlink to aparc.DKTatlas+aseg.mapped.mgz
mri	aparc+aseg.mgz	surface	symlink to aparc.DKTatlas+aseg.mapped.mgz
mri	aseg.mgz	surface	subcortical segmentation after running the surface module
mri	wmparc.DKTatlas.mapped.mgz	surface	white matter parcellation
mri	wmparc.mgz	surface	symlink to wmparc.DKTatlas.mapped.mgz
surf	lh.area, rh.area	surface	surface area overlay file
surf	lh.curv, rh.curv	surface	curvature overlay file
surf	lh.inflated, rh.inflated	surface	inflated cortical surface
surf	lh.pial, rh.pial	surface	pial surface
surf	lh.thickness, rh.thickness	surface	cortical thickness overlay file
surf	lh.volume, rh.volume	surface	gray matter volume overlay file
surf	lh.white, rh.white	surface	white matter surface
label	lh.aparc.DKTatlas.annot, rh.aparc.DKTatlas.annot	surface	symlink to lh.aparc.DKTatlas.mapped.annot
label	lh.aparc.DKTatlas.mapped.annot, rh.aparc.DKTatlas.mapped.annot	surface	annotation file for cortical parcellations, mapped from ASEGDKT segmentation to the surface
stats	aseg.stats	surface	table of cortical and subcortical segmentation statistics after running the surface module
stats	lh.aparc.DKTatlas.mapped.stats, rh.aparc.DKTatlas.mapped.stats	surface	table of cortical parcellation statistics, mapped from ASEGDKT segmentation to the surface
stats	lh.curv.stats, rh.curv.stats	surface	table of curvature statistics
stats	wmparc.DKTatlas.mapped.stats	surface	table of white matter segmentation statistics
scripts	recon-all.log	surface	logfile

Lesion Inpainting Tool (LIT, optional)

When --lesion_mask <path to file> is provided, FastSurfer wraps the segmentation and surface pipelines with lesion inpainting using LIT. The extension is currently experimental. It inpaints the lesion region, runs the requested FastSurfer modules on the inpainted image, and then maps the lesion back into the resulting outputs. The current LIT postprocessing workflow updates the primary FastSurfer files in place and keeps the original pre-lesion outputs either as .lit backups or, for some surface-derived files, in the original .mapped.* files.

For lesion mask requirements, see the FastSurfer-LIT module documentation.

Inpainting Outputs

These are the key files created during the initial inpainting stage. FastSurfer with LIT writes these outputs directly into the standard subject directory layout.

directory	filename	module	description
mri	inpainted.lit.nii.gz	lit	inpainted T1 image used for downstream processing
mri	mask.lit.nii.gz	lit	processed lesion mask in FastSurfer image space, after optional preprocessing
mri/orig	mask.lit.nii.gz	lit	original lesion mask stored in the subject directory
mri/orig	inpainting_original_image.lit.nii.gz	lit	conformed original image used internally by LIT
mri/orig	inpainting_masked_image.lit.nii.gz	lit	conformed masked image used internally by LIT
scripts	inpainting_*.lit.png	lit	preview images from the inpainting step

Postprocessing MRI Outputs

These files contain the lesion-integrated segmentations. LIT overwrites the primary FastSurfer outputs and stores the pre-lesion versions as .lit backups.

directory	filename	module	description
mri	aparc.DKTatlas+aseg.deep.mgz	lit	lesion-integrated whole-brain segmentation
mri	aparc.DKTatlas+aseg.deep.lit.mgz	lit	backup of the pre-lesion whole-brain segmentation
mri	aseg.auto_noCCseg.mgz	lit	lesion-integrated subcortical segmentation used for VINN statistics
mri	aseg.auto_noCCseg.lit.mgz	lit	backup of the pre-lesion subcortical segmentation
mri	cerebellum.CerebNet.nii.gz	lit	lesion-integrated cerebellum segmentation when CerebNet is available
mri	cerebellum.CerebNet.lit.nii.gz	lit	backup of the pre-lesion cerebellum segmentation
mri	hypothalamus.HypVINN.nii.gz	lit	lesion-integrated hypothalamus segmentation when HypVINN is available
mri	hypothalamus.HypVINN.lit.nii.gz	lit	backup of the pre-lesion hypothalamus segmentation

Postprocessing Statistics and Reports

LIT regenerates the relevant stats files after lesion mapping, keeps the pre-lesion versions as .lit backups where applicable, and writes lesion-specific reports.

directory	filename	module	description
stats	lesion_impact_summary.yaml	lit	machine-readable summary of affected brain regions
stats	aparc.DKTatlas+aseg.lesion_report.txt	lit	report of volumetric structures affected by the lesion
stats	aseg.lesion_report.txt	lit	report of affected structures in the FreeSurfer aseg segmentation
stats	aseg+DKT.VINN.stats	lit	lesion-integrated whole-brain/VINN summary statistics
stats	aseg+DKT.VINN.lit.stats	lit	backup of the pre-lesion whole-brain/VINN statistics
stats	aseg.VINN.stats	lit	lesion-integrated subcortical VINN statistics
stats	aseg.VINN.lit.stats	lit	backup of the pre-lesion subcortical VINN statistics
stats	cerebellum.CerebNet.stats	lit	lesion-integrated cerebellum statistics when CerebNet is available
stats	cerebellum.CerebNet.lit.stats	lit	backup of the pre-lesion cerebellum statistics
stats	hypothalamus.HypVINN.stats	lit	lesion-integrated hypothalamus statistics when HypVINN is available
stats	hypothalamus.HypVINN.lit.stats	lit	backup of the pre-lesion hypothalamus statistics

Surface-based Outputs

If the surface pipeline is run, LIT also updates the relevant surface annotations and stats. The public annotation paths are kept at the standard FreeSurfer names, while the preserved pre-lesion surface stats remain in the corresponding .mapped.stats files.

directory	filename	module	description
label	{lh,rh}.aparc.DKTatlas.annot	lit	cortical parcellation with lesion projected onto the surface; symlink to {lh,rh}.aparc.DKTatlas.mapped.annot
label	{lh,rh}.aparc.DKTatlas.lit.annot	lit	pre-lesion cortical parcellation; symlink to {lh,rh}.aparc.DKTatlas.mapped.lit.annot
stats	{lh,rh}.aparc.DKTatlas.stats	lit	lesion-integrated cortical surface statistics
stats	{lh,rh}.aparc.DKTatlas.mapped.stats	lit	backup of the pre-lesion cortical surface statistics
stats	{lh,rh}.aparc.DKTatlas.anatomy_report.txt	lit	report of cortical structures affected by the lesion

Longitudinal Processing

When running the longitudinal pipeline the output will be as above for the individual time point directories. Note that the templateID directory for the within-subject template will not contain all files and usually is not looked at or analyzed, as it represents an intermediate step in the longitudinal pipeline.