HippMetric: A skeletal-representation-based framework for cross-sectional and longitudinal hippocampal substructural morphometry
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908727667326976 |
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| author | Gao, Na Ye, Chenfei Yang, Yanwu Li, Anqi He, Zhengbo Liang, Li Liu, Zhiyuan Hao, Xingyu Ma, Ting Guo, Tengfei |
| author_facet | Gao, Na Ye, Chenfei Yang, Yanwu Li, Anqi He, Zhengbo Liang, Li Liu, Zhiyuan Hao, Xingyu Ma, Ting Guo, Tengfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HippMetric: A skeletal-representation-based framework for cross-sectional and longitudinal hippocampal substructural morphometry Gao, Na Ye, Chenfei Yang, Yanwu Li, Anqi He, Zhengbo Liang, Li Liu, Zhiyuan Hao, Xingyu Ma, Ting Guo, Tengfei Computer Vision and Pattern Recognition 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. |
| title | HippMetric: A skeletal-representation-based framework for cross-sectional and longitudinal hippocampal substructural morphometry |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.19214 |