AUTHOR=Khan Nawazish , Peterson Andrew C. , Aubert Benjamin , Morris Alan , Atkins Penny R. , Lenz Amy L. , Anderson Andrew E. , Elhabian Shireen Y. 

TITLE=Statistical multi-level shape models for scalable modeling of multi-organ anatomies

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1089113

DOI=10.3389/fbioe.2023.1089113

ISSN=2296-4185

ABSTRACT=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.