Physiology-Aware PolySnake for Coronary Vessel Segmentation

Coronary artery disease (CAD) is a significant health risk that requires early detection for effective treatment. While recent advances in deep learning have shown promise in automating CAD detection from coronary computed to-mography angiography (CCTA) images, the accurate segmentation of coronary vessels remains a challenge, particularly due to the imbalanced presence of plaque in unhealthy vessels. This paper introduces a physiology-aware approach<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/opensourcetorch/Physiology-aware-PolySnake to coronary vessel segmentation that addresses these challenges. Our proposed pipeline consists of three main components. First, a hybrid UNeXt architecture is designed to segment artery boundaries and predict initial boundary contours by leveraging 3D spatial relations among adjacent slices. Second, we introduce multi-class circular convolution for iterative contour deformation, which generates well-connected contour pairs of the artery wall's inner and outer boundaries through iterative refinement. Finally, we propose a focal smooth Lllossfunction to handle the implicit class imbalance caused by plaque in unhealthy vessels and to enhance the robustness of the physiology-aware polysnake network by explicitly limiting the accuracy of initial contours. Extensive evaluations demonstrate that our methods significantly improve model performance, achieving state-of-the-art results in coronary vessel segmentation.