Welcome to FastSurfer!

Overview

This README contains all information needed to run FastSurfer - a fast and accurate deep-learning based neuroimaging pipeline. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric analysis (within minutes) and surface-based thickness analysis (within only around 1h run time). FastSurfer is transitioning to sub-millimeter resolution support throughout the pipeline.

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

• approximately 5 minutes (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. 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).

3. 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 <path>, which will be registered to the T1w image (see --reg_mode option).

   • calculates volume statistics corrected for partial volume effects based on the T1w image (skipped if --no_bias_field is passed).

Surface reconstruction

• approximately 60-90 minutes, --surf_only runs only the surface part.

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

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. https://doi.org/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. http://dx.doi.org/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. https://doi.org/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 2023; 1 1–32. https://doi.org/10.1162/imag_a_00034

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

The recon-surf pipeline is largely based on FreeSurfer.