Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries

TL;DR

This paper introduces a novel statistical shape modeling approach for multi-organ anatomies with shared boundaries, exemplified on the biventricular heart, enabling detailed analysis of shape variations and shared boundary changes.

Contribution

It presents a flexible, data-driven method for modeling shared boundaries in complex anatomical structures, improving upon existing SSM techniques.

Findings

Successfully modeled biventricular heart and shared septum boundary

Captured morphological and alignment variations across population

Enhanced understanding of shape changes related to pathology

Abstract

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.