HippMetric: A skeletal-representation-based framework for cross-sectional and longitudinal hippocampal substructural morphometry

2025-12-22

AI Summary

High inter-subject and longitudinal anatomical variability, coupled with the hippocampus’s complex folding pattern, undermines point-to-point correspondence stability—impeding early detection of neurodegenerative disease. Method: We propose a novel morphometric framework based on skeletal representations (s-reps), introducing the Axis-Referenced Morphological Model (ARMM) and a functionally informed deformable skeletal coordinate system. For the first time, we embed the hippocampal lamellar architecture—characterized by lamellae oriented orthogonally to the long axis—into geometric modeling to ensure biologically interpretable, consistent localization. Our framework integrates surface reconstruction, non-rigid deformation, and a rib-optimization algorithm enforcing strict geometric constraints: boundary fidelity, orthogonality, and self-intersection avoidance. Results: Validated on two large international cohorts, our method significantly improves morphometric accuracy, test–retest reliability, and correspondence stability, outperforming state-of-the-art shape modeling approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate characterization of hippocampal substructure is crucial for detecting subtle structural changes and identifying early neurodegenerative biomarkers. However, high inter-subject variability and complex folding pattern of human hippocampus hinder consistent cross-subject and longitudinal analysis. Most existing approaches rely on subject-specific modelling and lack a stable intrinsic coordinate system to accommodate anatomical variability, which limits their ability to establish reliable inter- and intra-individual correspondence. To address this, we propose HippMetric, a skeletal representation (s-rep)-based framework for hippocampal substructural morphometry and point-wise correspondence across individuals and scans. HippMetric builds on the Axis-Referenced Morphometric Model (ARMM) and employs a deformable skeletal coordinate system aligned with hippocampal anatomy and function, providing a biologically grounded reference for correspondence. Our framework comprises two core modules: a skeletal-based coordinate system that respects the hippocampus' conserved longitudinal lamellar architecture, in which functional units (lamellae) are stacked perpendicular to the long-axis, enabling anatomically consistent localization across subjects and time; and individualized s-reps generated through surface reconstruction, deformation, and geometrically constrained spoke refinement, enforcing boundary adherence, orthogonality and non-intersection to produce mathematically valid skeletal geometry. Extensive experiments on two international cohorts demonstrate that HippMetric achieves higher accuracy, reliability, and correspondence stability compared to existing shape models.

Problem

• Develops skeletal framework for hippocampal substructure analysis across subjects
• Addresses anatomical variability hindering consistent cross-sectional and longitudinal studies
• Establishes reliable correspondence for morphometry despite complex hippocampal folding patterns

Innovation

• Skeletal coordinate system aligned with hippocampal anatomy
• Individualized skeletal representations via surface reconstruction and deformation
• Axis-Referenced Morphometric Model for cross-subject and longitudinal analysis