DEEP-GYRALNET: ENABLING MACHINE LEARNING IN GYRAL FOLDING PATTERN EXTRACTION ON CORTICAL SURFACE.

The 3-hinge gyrus (3HG), where three gyri converge, acts as a structural and functional hub in the brain. Connecting these hubs forms the GyralNet, but traditionally extraction requires complex geometric steps, limiting scalability. To address this challenge, we propose Deep-GyralNet, a novel deep learning framework for efficient 3HG and GyralNet extraction. Deep-GyralNet leverages a Spherical U-Net to learn gyral crest representations from cortical morphological features, coupled with a Connectivity-Aware Path Enforcement loss that enforces topological continuity of predicted ridges. A minimum spanning tree-based refinement is then applied to ensure structural connectivity and yield a topologically valid GyralNet. Experiments on the Human Connectome Project dataset demonstrate that Deep-GyralNet achieves an average completeness of 98%, while reducing processing time by 94% compared to the conventional pipeline. These establish Deep-GyralNet the first deep learning solution for fast and accurate 3HG identification, enabling large-scale studies of gyral folding networks in brain development, aging and disorders.