Bsc net improves small branch detection in coronary vessel images
BSC-Net: A Small-Branch-Sensitive Structural Continuity Network for Coronary Vessel Segmentation and Quantitative Angiographic Analysis
Computer Vision and Pattern Recognition
Summary
Detecting small blood vessels in heart X-ray images is hard because of blurry pictures and overlapping structures. The authors created BSC-Net, a special computer program that better finds tiny vessel branches and keeps vessel shapes connected. They tested BSC-Net on public datasets and showed it works better than earlier methods. This tool helps doctors measure heart vessel properties more accurately to assess heart disease.
What this means in practice
- •For medical imaging teams: Produce more accurate coronary vessel maps from X-ray images to aid clinical assessment of heart artery diseases.
- •For cardiovascular device developers: Use precise vessel segmentation to simulate and evaluate blood flow and device interactions in coronary arteries.
Authors
Wanxian Li, Jiaqian Qin, Qingyi Xian, Yazhi Li, Song Chen, Liman Li, Hao He
Abstract
Vessel segmentation in X-ray coronary angiography (XCA) is a fundamental step for quantitative coronary analysis and subsequent assessment of coronary artery disease. However, accurate vessel segmentation remains challenging because of imaging noise, complex bifurcations, and the overlap of vessels and background structures, which can lead to disrupted vascular connectivity and missed small branches. In this work, we propose BSC-Net, a ResNet-U-Net-based framework tailored to improve small-vessel representation and repair vascular structural continuity. BSC-Net enhances small-vessel representation through targeted sampling and improves vascular structural continuity by integrating long-range contextual modeling and Edge-Informed Loss (EIL). BSC-Net was validated on two public XCA datasets, demonstrating state-of-the-art (SOTA) performance in coronary vessel segmentation with Dice and IoU scores of 77.8%/90.6% and 64.5%/83.0%, respectively. Furthermore, based on the obtained vessel segmentation, we performed automated quantitative coronary analysis and derived clinically relevant morphological and hemodynamic parameters, including stenosis ratio, time-to-peak, and relative propagation velocity. These results demonstrate that BSC-Net produces accurate vessel segmentation results with preserved vascular continuity for quantitative coronary assessment, enabling reliable downstream analysis and clinical evaluation of coronary artery disease.