Xu Ruoxuan, Dai Longhui, Wang Jianru, Zhang Lei, Wang Yuanquan
School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China.
J Imaging. 2025 Jun 11;11(6):192. doi: 10.3390/jimaging11060192.
Coronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small vessel diameters, large morphological variations, low contrast, and motion artifacts, conventional segmentation methods, including classical image processing (such as region growing and level sets) and early deep learning models with limited receptive fields, are unsatisfactory. We propose SADiff, a hybrid framework that integrates a dilated attention network (DAN) for ROI extraction, a diffusion-based subnet for noise suppression in low-contrast regions, and a striped attention network (SAN) to refine tubular structures affected by morphological variations. Experiments on the public ImageCAS dataset show that it has a Dice score of 83.48% and a Hausdorff distance of 19.43 mm, which is 6.57% higher than U-Net3D in terms of Dice. The cross-dataset validation on the private ImageLaPP dataset verifies its generalizability with a Dice score of 79.42%. This comprehensive evaluation demonstrates that SADiff provides a more efficient and versatile method for coronary segmentation and shows great potential for improving the diagnosis and treatment of CAD.
冠状动脉疾病（CAD）是一种高度流行的心血管疾病，也是全球主要的死亡原因之一。从CT血管造影（CTA）图像中准确分割冠状动脉对于冠状动脉疾病的诊断和治疗至关重要。然而，由于血管直径小、形态变化大、对比度低以及运动伪影，包括经典图像处理（如区域生长和水平集）和具有有限感受野的早期深度学习模型在内的传统分割方法并不令人满意。我们提出了SADiff，这是一个混合框架，它集成了用于ROI提取的扩张注意力网络（DAN）、用于低对比度区域噪声抑制的基于扩散的子网以及用于细化受形态变化影响的管状结构的条纹注意力网络（SAN）。在公共ImageCAS数据集上的实验表明，它的Dice分数为83.48%，豪斯多夫距离为19.43毫米，在Dice方面比U-Net3D高6.57%。在私有ImageLaPP数据集上的跨数据集验证以79.42%的Dice分数验证了其通用性。这种综合评估表明，SADiff为冠状动脉分割提供了一种更高效、更通用的方法，并显示出在改善CAD诊断和治疗方面具有巨大潜力。
