Duan Hualin, Yi Sanli, Ren Yanyou
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Key Laboratory of Computer Technology Application of Yunnan Province, Kunming 650500, China.
Sensors (Basel). 2024 Dec 20;24(24):8134. doi: 10.3390/s24248134.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI). This new framework is called DCA-YOLOv8. The experimental results show that the DCA-YOLOv8 framework performs better than existing object detection algorithms in coronary artery stenosis detection, with precision, recall, F1-score and mean average precision (mAP) at 96.62%, 95.06%, 95.83% and 97.6%, respectively. In addition, the framework performs better in the classification task, with accuracy at 93.2%, precision at 92.94%, recall at 93.5% and F1-score at 93.22%. Despite the limitations of data volume and labeled data, the proposed framework is valuable in applications for assisting the cardiac team in making decisions by using coronary angiography results.
由于血管结构复杂、成像图片质量差、呼吸伪影导致血管轮廓不佳以及狭窄病变常出现在图像的小区域内，冠状动脉狭窄检测仍然是一项具有挑战性的任务。为了提高检测的准确性和效率，本文提出了一种基于冠状动脉狭窄检测框架（DCA-YOLOv8）的新型深度学习技术。该框架由直方图均衡化和Canny边缘检测预处理（HEC）增强模块、双坐标注意力（DCA）特征提取模块以及一个结合了新设计的损失函数（称为自适应内部CIoU（AICI））的输出模块组成。这个新框架被称为DCA-YOLOv8。实验结果表明，DCA-YOLOv8框架在冠状动脉狭窄检测中比现有目标检测算法表现更好，精确率、召回率、F1分数和平均精度均值（mAP）分别为96.62%、95.06%、95.83%和97.6%。此外，该框架在分类任务中表现也更好，准确率为93.2%，精确率为92.94%，召回率为93.5%，F1分数为93.22%。尽管存在数据量和标注数据的局限性，但所提出的框架在利用冠状动脉造影结果协助心脏团队做出决策的应用中具有价值。
