Skeletal modeling of the tricuspid valve via cylindrical parameterization

Jared Vicory; Christian Herz; Maura Flynn; Alana Cianciulli; Patricia Sabin; Andras Lasso; Matthew A. Jolley; Beatriz Paniagua

doi:10.1117/12.2654364

3 April 2023 Skeletal modeling of the tricuspid valve via cylindrical parameterization

Jared Vicory, Christian Herz, Maura Flynn, Alana Cianciulli, Patricia Sabin, Andras Lasso, Matthew A. Jolley, Beatriz Paniagua

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246431 (2023) https://doi.org/10.1117/12.2654364
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Hypoplastic left heart syndrome is a severe congenital heart defect requiring surgical intervention shortly after birth. The surgery is complex and often leads to complications requiring additional surgeries. Understanding the relationship between the structure of the tricuspid valve and functional complications could lead to more powerful diagnostic and treatment options. Because the tricuspid valve does not have spherical topology, many traditional methods for creating boundary-based or skeleton-based shape models that require spherical parameterization of an object are not applicable unless individual leaflets are independently parameterized and then merged in a multi-object model. Instead we propose to create skeletal models (s-reps) of the entire tricuspid valve structure using a cylindrical parameterization. We modify a traditional cylindrical parameterization approach by adaptively changing angle sampling based on landmarks to produce anatomically relevant correspondence across a population of objects. From this we derive s-reps which yield an improved shape space and classification performance compared with previous approaches.

Citation Download Citation

Jared Vicory, Christian Herz, Maura Flynn, Alana Cianciulli, Patricia Sabin, Andras Lasso, Matthew A. Jolley, and Beatriz Paniagua "Skeletal modeling of the tricuspid valve via cylindrical parameterization", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246431 (3 April 2023); https://doi.org/10.1117/12.2654364

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