Segmentation of supragranular and infragranular layers in ultra-high resolution 7Tex vivoMRI of the human cerebral cortex

Abstract

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Lever-aging recent advancements in ultra-high resolutionex vivoMRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers inex vivoMRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphereex vivoscans at 120µm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.