FastSurfer is a fast and extensively validated deep-learning pipeline for the fully automated processing of structural human brain MRIs. As such, it provides FreeSurfer conform outputs, enables efficient big-data analysis for large cohort studies, and time-critical clinical applications such as structure localization during image acquisition or rapid extraction of quantitative measures for precision medicine.

FastSurfer consists of two consecutive main components:

  1. FastSurferCNN - an advanced deep learning architecture capable of whole brain segmentation into 95 classes in under 1 minute (on the GPU), mimicking FreeSurfer’s anatomical segmentation and cortical parcellation (DKTatlas)

  2. recon-surf - full FreeSurfer alternative for cortical surface reconstruction, mapping of cortical labels and traditional point-wise and ROI thickness analysis in approximately 60 minutes (+ optionally 30 min for group registration).

The project is Open Source and can be found at

Enjoy and let us know what you think …

Project Author(s)

Martin Reuter

Project Video

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