Welcome to FastSurfer!

Overview

FastSurfer is a fast and accurate deep-learning based neuroimaging pipeline. It provides a fully compatible FreeSurfer alternative for volumetric analysis (within minutes) and surface-based thickness analysis (in about half an hour), and it supports sub-millimeter resolutions down to 0.7mm (see the modules below for details).

The FastSurfer pipeline consists of two main parts for segmentation and surface reconstruction.

• the segmentation sub-pipeline (seg) employs advanced deep learning networks for fast, accurate segmentation and volumetric calculation of the whole brain and selected substructures.

• the surface sub-pipeline (recon-surf) reconstructs cortical surfaces, maps cortical labels and performs a traditional point-wise and ROI thickness analysis.

Segmentation Modules

• a few minutes on a GPU, --seg_only only runs this part.

Modules (all run by default):

1. asegdkt: FastSurferVINN for whole brain segmentation (deactivate with --no_asegdkt)

   • the core, outputs anatomical segmentation and cortical parcellation and statistics of 95 classes, mimics FreeSurfer’s DKTatlas.

   • requires a T1w image (notes on input images), supports high-res (up to 0.7mm, experimental beyond that).

   • performs bias-field correction and calculates volume statistics corrected for partial volume effects (skipped if --no_biasfield is passed).

2. cc: CorpusCallosum for corpus callosum segmentation and shape analysis (deactivate with --no_cc)

   • requires asegdkt_segfile (segmentation) and orig.mgz from the segmentation stage. In the standard pipeline this image is in FastSurfer conform space; with --seg_only --keepgeom it stays in native geometry, with only intensity scaling and dtype conversion as needed. Outputs include CC segmentation, thickness, and shape metrics.

   • standardizes brain orientation based on AC/PC landmarks (orient_volume.lta).

3. cereb: CerebNet for cerebellum sub-segmentation (deactivate with --no_cereb)

   • requires asegdkt_segfile, outputs cerebellar sub-segmentation with detailed WM/GM delineation.

   • requires a T1w image (notes on input images), which will be resampled to 1mm isotropic images (no native high-res support).

   • calculates volume statistics corrected for partial volume effects (skipped if --no_biasfield is passed).

4. hypothal: HypVINN for hypothalamus subsegmentation (deactivate with --no_hypothal)

   • outputs a hypothalamic subsegmentation including 3rd ventricle, c. mammilare, fornix and optic tracts.

   • a T1w image is highly recommended (notes on input images), supports high-res (up to 0.7mm, but experimental beyond that).

   • allows the additional passing of a T2w image with --t2 <t2_path>, which will be registered to the T1w image (see --reg_mode option).

   • calculates summary statistics based on the biasfield-corrected T1w image (skipped if --no_biasfield is passed).

Surface reconstruction

• approximately 20 to 40 minutes with both hemispheres in parallel (the default), --surf_only runs only the surface part.

• supports high-resolution images (up to 0.7mm, experimental beyond that).

• requires a FreeSurfer license file as it uses some FreeSurfer binaries internally.

• requires outputs of the asegdkt and the cc modules as a prerequisite (can be included in the same run).

Extensions

• FastSurfer-LIT wraps the FastSurfer segmentation and surface pipelines with lesion inpainting when a lesion mask is provided via --lesion_mask <lesion_mask_path>. Review LIT-modified outputs before using them for downstream analyses.

Get started

Quick Start

Segment an example image in a few minutes.

Quick Start

Installation

Install FastSurfer on macOS, Linux or Windows.

Installation

Examples

Run the full pipeline, many subjects, or on a cluster.

Examples

Output files

Find the segmentations, surfaces and statistics.

Output files

References

If you use this for research publications, please cite:

• Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219 (2020), 117012. doi:10.1016/j.neuroimage.2020.117012

• Henschel L*, Kuegler D*, Reuter M. (*co-first) FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods - A Solution for HighRes Brain MRI. NeuroImage 251 (2022), 118933. doi:10.1016/j.neuroimage.2022.118933

• Faber J*, Kuegler D*, Bahrami E*, et al. (*co-first) CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. NeuroImage 264 (2022), 119703. doi:10.1016/j.neuroimage.2022.119703

• Estrada S, Kuegler D, Bahrami E, Xu P, Mousa D, Breteler MMB, Aziz NA, Reuter M. FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI. Imaging Neuroscience 1 (2023), 1–32. doi:10.1162/imag_a_00034

• Pollak C, Diers K, Estrada S, Kuegler D, Reuter M. FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry. Imaging Neuroscience (2026). doi:10.1162/IMAG.a.1221

If you use the lesion inpainting extension, please also cite:

• Pollak C, Kuegler D, Bauer T, Rueber T, Reuter M. FastSurfer-LIT: Lesion Inpainting Tool for Whole Brain MRI Segmentation with Tumors, Cavities and Abnormalities. Imaging Neuroscience (2025). doi:10.1162/imag_a_00446

Stay tuned for updates and follow us on X/Twitter.

Acknowledgements

This project is partially funded by:

• Chan Zuckerberg Initiative

• German Federal Ministry of Education and Research

• Helmholtz Software Award

The recon-surf pipeline is largely based on FreeSurfer.