Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach.

ABSTRACT: Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atl

SUBMITTER: Adams J 

PROVIDER: S-EPMC10122954 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature