@inproceedings{7048bceca43040eea5e39619d77c1c8a,
title = "Hierarchical Geodesic Polynomial Model for Multilevel Analysis of Longitudinal Shape",
abstract = "Longitudinal analysis is a core aspect of many medical applications for understanding the relationship between an anatomical subject{\textquoteright}s function and its trajectory of shape change over time. Whereas mixed-effects (or hierarchical) modeling is the statistical method of choice for analysis of longitudinal data, we here propose its extension as hierarchical geodesic polynomial model (HGPM) for multilevel analyses of longitudinal shape data. 3D shapes are transformed to a non-Euclidean shape space for regression analysis using geodesics on a high dimensional Riemannian manifold. At the subject-wise level, each individual trajectory of shape change is represented by a univariate geodesic polynomial model on timestamps. At the population level, multivariate polynomial expansion is applied to uni/multivariate geodesic polynomial models for both anchor points and tangent vectors. As such, the trajectory of an individual subject{\textquoteright}s shape changes over time can be modeled accurately with a reduced number of parameters, and population-level effects from multiple covariates on trajectories can be well captured. The implemented HGPM is validated on synthetic examples of points on a unit 3D sphere. Further tests on clinical 4D right ventricular data show that HGPM is capable of capturing observable effects on shapes attributed to changes in covariates, which are consistent with qualitative clinical evaluations. HGPM demonstrates its effectiveness in modeling shape changes at both subject-wise and population levels, which is promising for future studies of the relationship between shape changes over time and the level of dysfunction severity on anatomical objects associated with disease.",
keywords = "geodesic regression, hierarchical modeling, longitudinal data, statistical shape analysis",
author = "Ye Han and Jared Vicory and Guido Gerig and Patricia Sabin and Hannah Dewey and Silvani Amin and Ana Sulentic and Christian Hertz and Matthew Jolley and Beatriz Paniagua and James Fishbaugh",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 28th International Conference on Information Processing in Medical Imaging, IPMI 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1007/978-3-031-34048-2_62",
language = "English (US)",
isbn = "9783031340475",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "810--821",
editor = "Alejandro Frangi and {de Bruijne}, Marleen and Demian Wassermann and Nassir Navab",
booktitle = "Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings",
address = "Germany",
}