# FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline

arXiv: https://arxiv.org/abs/1910.03866 · HTML: https://mlsanity.com/paper/1910.03866/

## Key facts

- Cited by 112 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 30 of 112)

- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks](https://mlsanity.com/paper/2203.09446/) (2022)
- [CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs](https://mlsanity.com/paper/2202.08329/) (2022)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data](https://mlsanity.com/paper/2211.04234/) (2022)
- [A Study of Demographic Bias in CNN-based Brain MR Segmentation](https://mlsanity.com/paper/2208.06613/) (2022)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI](https://mlsanity.com/paper/2312.05119/) (2023)
- [Neural deformation fields for template-based reconstruction of cortical surfaces from MRI](https://mlsanity.com/paper/2401.12938/) (2024)
- [A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI](https://mlsanity.com/paper/2311.06631/) (2023)
- [CorticalFlow++: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability](https://mlsanity.com/paper/2206.06598/) (2022)
- [Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging](https://mlsanity.com/paper/2311.16914/) (2023)
- [Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS](https://mlsanity.com/paper/2503.01075/) (2025)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2308.07003/) (2023)
- [Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI](https://mlsanity.com/paper/2409.03889/) (2024)
- [From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows](https://mlsanity.com/paper/2601.09191/) (2026)
- [Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing](https://mlsanity.com/paper/2205.11346/) (2022)
- [Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://mlsanity.com/paper/2402.06190/) (2024)
- [VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis](https://mlsanity.com/paper/2410.08397/) (2024)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)
- [CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction](https://mlsanity.com/paper/2206.02374/) (2022)
- [Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data](https://mlsanity.com/paper/2211.02400/) (2022)
- [Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation](https://mlsanity.com/paper/2211.05421/) (2022)
- [Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction](https://mlsanity.com/paper/2307.12299/) (2023)
- [POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration](https://mlsanity.com/paper/2407.03922/) (2024)
- [Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide](https://mlsanity.com/paper/2304.05901/) (2023)

## Abstract (arXiv)

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and sub-cortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 minute) and surface-based thickness analysis (within only around 1h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.