血管介入手术导丝末端检测方法研究

介入手术导丝的末端检测是保证手术精准控制和安全性的关键，本文针对术中导丝末端检测的临床需求，提出一种基于改进YOLOv4Tiny网络的导丝末端检测方法。该方法基于YOLOv4Tiny网络架构，通过优化特征提取网络中的残差结构，增加注意力机制和混合膨胀卷积网络，实现算法对小目标特征提取能力和检测精度的提升、感受野的扩大，且在保证图像的分辨率的同时不增加计算量。为了验证本文改进算法的有效性，对算法在构建数据集和实际手术数据集中分别进行了测试。实验结果表明：本文改进算法在构建数据集中的平均精度可达97.6%,导丝末端的检测误差不足5%,在实际手术数据集中的平均精度为92.8%。本文改进算法为介入手术导丝的末端检测提供了有效方法，在生物医学机器人等领域具有广阔的应用前景。

Study on the detection method of putting guide wire endin vascular interventional surgery

The end detection of guide wire in interventional surgery plays a vital role in ensuring the accurate control and safety of surgery. In this article, a method of end detection of guide wire based on the improved YOLOv4Tiny network is proposed for the clinical demand of end detection of guide wire during surgery. The YOLOv4Tiny network architecture is utilized in the proposed method. By optimizing the residual structure in the feature extraction network, and enhancing the attention mechanism and the hybrid expansion convolution network, the small target feature extraction ability and detection accuracy are greatly improved. The receptive field is also expanded, which ensures the image resolution without increasing the computation amount. To evaluate the effectiveness of the improved algorithm, it is tested in the constructed dataset and the actual surgical dataset. According to the experimental results, the average accuracy of the improved algorithm in the constructed dataset reaches 97. 6% , with the detection error of the guide wire end be less than 5% , and the average accuracy in the actual surgical dataset is 92. 8% . The improved algorithm is of great reference significance for the end detection of interventional surgical guide wire, which has broad application prospects in fields related to biomedical robots.