Physiology-Aware PolySnake for Coronary Vessel Segmentation

Abstract

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 tomography 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 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 L1 loss function 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.