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Hierarchical Geodesic Polynomial Model for Multilevel Analysis of Longitudinal Shape
Ye Han1, Jared Vicory1, Guido Gerig2
1Kitware, Inc., Clifton Park, NY, 12065, USA.
Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 7, 2023
Summary
We introduce the hierarchical geodesic polynomial model (HGPM) for analyzing longitudinal shape changes in anatomical subjects. This method accurately models individual and population-level shape trajectories over time, enhancing medical applications.
Area of Science:
- Medical imaging analysis
- Biostatistics
- Computational anatomy
Background:
- Longitudinal analysis is crucial for understanding anatomical shape changes over time.
- Mixed-effects modeling is standard for longitudinal data but has limitations for complex shape analysis.
Purpose of the Study:
- To extend mixed-effects modeling for multilevel analyses of longitudinal shape data.
- To introduce the hierarchical geodesic polynomial model (HGPM) for accurate shape trajectory modeling.
Main Methods:
- Transforming 3D shapes into a non-Euclidean shape space for regression.
- Utilizing geodesics on a Riemannian manifold for shape analysis.
- Applying univariate geodesic polynomial models at the subject level and multivariate expansion at the population level.
Main Results:
- HGPM accurately models individual shape change trajectories with fewer parameters.
- Population-level effects of covariates on shape trajectories are effectively captured.
- Validation on synthetic data and clinical 4D right ventricular data confirms HGPM's capability.
Conclusions:
- HGPM is effective for modeling shape changes at both subject-wise and population levels.
- The method shows promise for studying the relationship between shape changes and disease severity.
- HGPM offers a powerful tool for advanced longitudinal shape analysis in medical research.
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