Intraoperative detection and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware, such as tracking systems or the robot encoders, are cumbersome and lack accuracy, surgical vision is evolving as a promising technique to detect and track the instruments using only endoscopic images. The present paper presents a review of the literature regarding image-based laparoscopic tool detection and tracking using convolutional neural networks (CNNs) and consists of four primary parts: (1) fundamentals of CNN; (2) public datasets; (3) CNN-based methods for the detection and tracking of laparoscopic instruments; and (4) discussion and conclusion. To help researchers quickly understand the various existing CNN-based algorithms, some basic information and a quantitative estimation of several performances are analyzed and compared from the perspective of ‘partial CNN approaches’ and ‘full CNN approaches’. Moreover, we highlight the challenges related to research of CNN-based detection algorithms and provide possible future developmental directions.

术中检测和追踪微创器械是计算机和机器人辅助手术的前提。由于诸如跟踪系统或机器人编码器之类的附加硬件笨重且缺乏准确性，因此手术视觉正在发展成为一种仅使用内窥镜图像来检测和跟踪器械的有前途的技术。本文介绍了有关使用卷积神经网络（CNN）进行基于图像的腹腔镜工具检测和跟踪的文献综述，它包括四个主要部分：（1）CNN的基础；（2）公共数据集；（3）基于CNN的腹腔镜器械检测和跟踪方法；（4）讨论与结论。为了帮助研究人员快速了解现有的各种基于CNN的算法，从“部分CNN方法”和“完整CNN方法”的角度分析和比较了一些基本信息和几种性能的定量估计。此外，我们重点介绍了与基于CNN的检测算法研究相关的挑战，并提供了可能的未来发展方向。