用心脏区域引导网络提升冠脉血管分割与狭窄评估精度
Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
- 引入心肌区域引导模块，融合多尺度特征增强定位能力
- 分割Dice达85.04%，狭窄检出真阳性率提升5.46%优于3D U-Net
- 支持不确定性量化，适合临床辅助诊断与精准医疗场景
冠状动脉疾病（CAD）是全球主要致死病因之一，需借助冠状动脉计算机断层扫描血管造影（CCTA）实现精准分割与狭窄检测。现有方法在低对比度、形态变异和小血管分割方面存在挑战。本文提出心肌区域引导特征聚合网络（MGFA-Net），一种新型双编码器U型架构，融合解剖先验知识以增强鲁棒性。框架包含三项创新：（1）心肌区域引导模块，通过心肌轮廓扩展与多尺度特征融合引导注意力至冠脉区域；（2）残差特征提取编码模块，结合并行空间通道注意力与残差块提升局部-全局特征区分能力；（3）多尺度特征融合模块，实现层级血管特征的自适应聚合。此外，蒙特卡洛丢弃法量化预测不确定性，提升临床可解释性。对于狭窄检测，采用基于形态学的中心线提取算法将血管树分割为解剖分支，实现截面面积量化与狭窄分级。MGFA-Net在测试中取得Dice分数85.04%、准确率84.24%、HD95为6.1294 mm，且狭窄检测真阳性率较3D U-Net提升5.46%。该集成分割-狭窄评估流程实现了自动化、可解释的CAD评估，推动深度学习与解剖先验结合向精准医疗迈进。代码已开源于http://github.com/chenzhao2023/MGFA_CCTA。
原文摘要 · Abstract (English)
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://github.com/chenzhao2023/MGFA_CCTA
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