Statistical multi-level shape models for scalable modeling of multi-organ anatomies - Khan, Nawazish; Peterson, Andrew C.; Aubert, Benjamin; Morris, Alan; Atkins, Penny R.; Lenz, Amy L.; Anderson, Andrew E.; Elhabian, Shireen Y. (Frontiers in Bioengineering and Biotechnology, 2023)
Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables learning population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics.