FastSurfer - A fast and accurate deep learning based neuroimaging pipeline

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 subcortical 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 min) and surface-based thickness analysis (within only around 1 h 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.

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Cited by papers we hold 4

• Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets · 2023
  No notice
• The OpenNeuro resource for sharing of neuroscience data · 2021
  No notice
• Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal · 2021
  No notice
• The ANTsX ecosystem for quantitative biological and medical imaging · 2021
  No notice

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