FastSurfer - A fast and accurate deep learning based neuroimaging pipeline

• Published in: NeuroImage (Orlando, Fla.) Vol. 219; p. 117012
• Main Authors: Henschel, Leonie, Conjeti, Sailesh, Estrada, Santiago, Diers, Kersten, Fischl, Bruce, Reuter, Martin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2020; Elsevier Limited; Elsevier
• Subjects: Artificial intelligence; Biomarkers; Brain - diagnostic imaging; Brain architecture; Brain mapping; Computational neuroimaging; Deep Learning; Dementia disorders; Embedding; Freesurfer; Humans; Image processing; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neuroimaging; Neuroimaging - methods; Pipelines; Reproducibility of Results; Segmentation; Software; Structural MRI; United States > US; Montana; Deep learning; Structural MRI; Freesurfer; Artificial intelligence; Computational neuroimaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117012

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. [Display omitted]