Abstract
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 this http URL
Abstract (translated)
冠状动脉疾病（CAD）仍然是全球主要的死亡原因之一，需要通过冠状动脉计算机断层血管造影（CCTA）进行准确的分割和狭窄检测。现有方法在面对低对比度、形态变化及小血管分割等挑战时存在困难。为解决这些限制，我们提出了心肌区域引导特征聚合网络（Myocardial Region-guided Feature Aggregation Net, MGFA-Net），这是一种新颖的U形双编码器架构，通过整合解剖先验知识来增强冠状动脉分割的鲁棒性。 我们的框架包括三个关键创新： 1. 心肌区域导向模块：通过心肌轮廓扩展和多尺度特征融合将注意力集中在冠状动脉区域。 2. 残差特征提取编码模块：结合并行空间通道注意机制与残差块，增强局部-全局特征区分度。 3. 多尺度特征融合模块：用于自适应聚合分层血管特征。 此外，通过蒙特卡洛丢弃法量化预测不确定性，支持临床可解释性。对于狭窄检测，我们采用了一种基于形态学的中心线提取算法来将血管树划分为解剖分支，从而实现横截面面积定量和狭窄分级。MGFA-Net的优势在于其在Dice得分（85.04%）、准确率（84.24%）及HD95值（6.1294毫米）上的优异表现，并且在狭窄检测的真阳性率上相较于3D U-Net提升了5.46%，表明了其在冠状动脉疾病评估中的优越性。 该集成分割至狭窄检测管道提供了一种自动化的、临床可解释的CAD评估方法，将深度学习与解剖先验知识相结合，为精准医疗提供了支持。我们的代码公开可用，在此链接中可以找到：[此处插入URL]
URL
https://arxiv.org/abs/2504.19300
