AUTHOR=Iyer Krithika , Morris Alan , Zenger Brian , Karanth Karthik , Khan Nawazish , Orkild Benjamin A. , Korshak Oleksandre , Elhabian Shireen 

TITLE=Statistical shape modeling of multi-organ anatomies with shared boundaries

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.1078800

DOI=10.3389/fbioe.2022.1078800

ISSN=2296-4185

ABSTRACT=Introduction:  Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some 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. A few examples of complex anatomical structures are the hip joint, a ball-and-socket synovial joint (the ball is the femoral head, and the socket is the acetabulum), the sacroiliac (SI) joint, a diarthrodial articular joint between the sacrum and the ilium, and the heart, a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within these shared boundaries, i.e., the cartilage of the hip joint, the sacroiliac joint, or the heart's septum, can indicate potential pathologic changes such as osteoarthritis in the hip joint, the sacroiliac joint, and right ventricular overload in the case of the heart. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. Nonetheless, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. The proposed workflow entails a method for the extraction of the shared boundaries of complex anatomical structures and a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population.