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

TL;DR

FastSurfer is a deep learning neuroimaging pipeline that significantly accelerates brain MRI analysis, achieving comparable accuracy to traditional methods while reducing processing time from hours to minutes.

Contribution

The paper introduces a novel deep learning architecture for fast, accurate brain MRI segmentation and surface reconstruction, replacing traditional, time-consuming pipelines.

Findings

Achieves full brain segmentation in under 1 minute.

Demonstrates high accuracy and reliability across multiple datasets.

Sensitive to group differences in dementia studies.

Abstract

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.