Huang Baixiang, Luo Yu, Wei Guangyu, He Songyan, Shao Yushuang, Zeng Xueying, Zhang Qing
School of Mathematical Sciences, Ocean University of China, Qingdao, China.
School of Haide, Ocean University of China, Qingdao, China.
Med Phys. 2025 Jul;52(7):e17970. doi: 10.1002/mp.17970.
Coronary artery disease (CAD) is a leading cause of cardiovascular-related mortality, and accurate stenosis detection is crucial for effective clinical decision-making. Coronary angiography remains the gold standard for diagnosing CAD, but manual analysis of angiograms is prone to errors and subjectivity.
This study aims to develop a deep learning-based approach for the automatic segmentation of coronary arteries from angiographic images and the quantitative detection of stenosis, thereby improving the accuracy and efficiency of CAD diagnosis.
We propose a novel deep learning-based method for the automatic segmentation of coronary arteries in angiographic images, coupled with a dynamic cohort method for stenosis detection. The segmentation model combines the MedSAM and VM-UNet architectures to achieve high-performance results. After segmentation, the vascular centerline is extracted, vessel diameter is computed, and the degree of stenosis is measured with high precision, enabling accurate identification of arterial stenosis.
On the mixed dataset (including the ARCADE, DCA1, and GH datasets), the model achieved an average IoU of 0.6308, with sensitivity and specificity of 0.9772 and 0.9903, respectively. On the ARCADE dataset, the average IoU was 0.6303, with sensitivity of 0.9832 and specificity of 0.9933. Additionally, the stenosis detection algorithm achieved a true positive rate of 0.5867 and a positive predictive value of 0.5911, demonstrating the effectiveness of our model in analyzing coronary angiography images.
SAM-VMNet offers a promising tool for the automated segmentation and detection of coronary artery stenosis. The model's high accuracy and robustness provide significant clinical value for the early diagnosis and treatment planning of CAD. The code and examples are available at https://github.com/qimingfan10/SAM-VMNet.
冠状动脉疾病（CAD）是心血管相关死亡的主要原因，准确检测狭窄对于有效的临床决策至关重要。冠状动脉造影仍然是诊断CAD的金标准，但血管造影的手动分析容易出错且具有主观性。
本研究旨在开发一种基于深度学习的方法，用于从血管造影图像中自动分割冠状动脉并定量检测狭窄，从而提高CAD诊断的准确性和效率。
我们提出了一种基于深度学习的新型方法，用于血管造影图像中冠状动脉的自动分割，并结合动态队列方法进行狭窄检测。分割模型结合了MedSAM和VM-UNet架构以实现高性能结果。分割后，提取血管中心线，计算血管直径，并高精度测量狭窄程度，从而能够准确识别动脉狭窄。
在混合数据集（包括ARCADE、DCA1和GH数据集）上，该模型的平均交并比为0.6308，灵敏度和特异性分别为0.9772和0.9903。在ARCADE数据集上，平均交并比为0.6303，灵敏度为0.9832，特异性为0.9933。此外，狭窄检测算法的真阳性率为0.5867，阳性预测值为0.5911，证明了我们的模型在分析冠状动脉造影图像方面的有效性。
SAM-VMNet为冠状动脉狭窄的自动分割和检测提供了一个有前景的工具。该模型的高精度和鲁棒性为CAD的早期诊断和治疗规划提供了重要的临床价值。代码和示例可在https://github.com/qimingfan10/SAM-VMNet获取。
