mlsanityData to 14 Oct 2026

BSC-Net: A Small-Branch-Sensitive Structural Continuity Network for Coronary Vessel Segmentation and Quantitative Angiographic Analysis

arXiv 2609.15400 ↗First posted 14 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

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.

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References · 17

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  10. 2018Boundary loss for highly unbalanced segmentationcited by 190
  11. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  12. 2019Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networkscited by 116
5 more
  1. 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
  2. 2024Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structurescited by 43
  3. 2023Directional Connectivity-based Segmentation of Medical Imagescited by 30
  4. 2020An Elastic Interaction-Based Loss Function for Medical Image Segmentationcited by 9
  5. 2022A Review of Modern Approaches for Coronary Angiography Imaging Analysiscited by 2

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