基于神经网络的医用视网膜手术针头检测
The Neural Networks Based Needle Detection for Medical Retinal Surgery
摘要：
近年来，深度学习技术发展迅速，深度神经网络在医学图像处理领域的应用成为聚光灯下的焦点。本文旨在采用基于YOLOv5的目标检测算法作为基本深度神经网络模型，实现医学视网膜手术中的针位检测。用于医疗手术的最先进的针检测方法主要集中于针结构分割。在针检测过程中，本文提出的方法包含角度检查，而不是针分割。该方法还采用了一种新的分类方法的基础上的不同位置的针，以改善模型。实验表明，该网络能够准确地检测出织针位置和测量出织针角度。所提出的方法的性能测试达到4.80之间的平均欧几里德距离检测到的尖端位置和实际的尖端位置。它还获得了所有测试集的头端角度的平均误差为0.85度。
英文摘要：
In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
