针对冠脉造影中细小血管分割难题，提出轻量级混合网络与自适应损失函数。
HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation
- 融合CNN局部特征提取与Transformer全局建模，设计多尺度注意力机制。
- 在私有数据集上优于14种主流方法，仅0.81M参数且显著提升细血管连续性。
- 自适应边界加权损失可跨架构提升分割精度，适合临床辅助诊断场景。
准确的冠状动脉数字减影血管造影（DSA）血管分割对冠心病的计算机辅助诊断与治疗规划至关重要。然而，细小低对比度血管、背景干扰及严重的血管-背景类别不平衡，使得弱远端分支和血管边界的可靠分割极具挑战。现有方法难以兼顾全局上下文推理与细血管、血管连续性及精细边界的保持。为此，本文提出HTC-SGA Former，一种用于冠状动脉DSA血管分割的轻量化混合Transformer-CNN框架。该模型采用CNN编码器提取局部血管形态特征，使用Transformer解码器进行上下文特征建模。提出的多尺度全局-局部窗口注意力（MS-GLWA）块实现高效的全局-局部上下文建模，自引导特征注意力（SGFA）模块增强弱血管响应。此外，边界加权自适应复合损失（BWACL）强化细血管边界，并自适应平衡血管恢复与边界优化。在私有右冠和左冠动脉DSA子集上的实验表明，HTC-SGA Former超越14种先进分割方法，同时保持紧凑结构，仅含0.81M参数。BWACL在四种编码器-解码器架构上均优于二元交叉熵与Dice损失，展现出强跨骨干适用性。HTC-SGA Former通过互补的全局-局部上下文建模、血管聚焦优化与自适应调优，显著提升细血管恢复、血管连续性与边界定位能力，支持未来心血管介入手术的可靠、高效计算辅助分析。
原文摘要 · Abstract (English)
Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interference, and severe vessel-background class imbalance make reliable segmentation of weak distal branches and vessel boundaries challenging. Existing methods struggle to balance global contextual reasoning with preservation of weak vessels, vessel continuity, and fine boundaries. To address these limitations, we propose HTC-SGA Former, a lightweight hybrid Transformer-CNN framework for coronary DSA vessel segmentation. It employs a CNN encoder for local vessel morphology extraction and a Transformer decoder for contextual feature modeling. A Multi-Scale Global-Local Window Attention (MS-GLWA) block performs efficient global-local contextual modeling, while a Self-Guided Feature Attention (SGFA) module enhances weak-vessel responses. In addition, a Boundary-Weighted Adaptive Compound Loss (BWACL) emphasizes thin-vessel boundaries and adaptively balances vessel recovery and boundary refinement. Experiments on private right and left coronary artery DSA subsets show that HTC-SGA Former outperforms 14 state-of-the-art segmentation methods while maintaining a compact architecture with only 0.81M parameters. BWACL also improves performance over binary cross-entropy and Dice losses across four encoder-decoder architectures, demonstrating strong cross-backbone applicability. HTC-SGA Former improves thin-vessel recovery, vessel continuity, and boundary localization through complementary global-local contextual modeling, vessel-focused refinement, and adaptive optimization, supporting reliable and computationally efficient coronary vessel analysis for future computer-assisted cardiovascular interventions.
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