School of Control Science and Engineering, Shandong University, Jinan, China.
Department of General surgery, First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Comput Assist Surg (Abingdon). 2020 Dec;25(1):15-28. doi: 10.1080/24699322.2020.1801842.
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.
术中微创器械的检测和跟踪是计算机辅助和机器人辅助手术的前提。由于额外的硬件，如跟踪系统或机器人编码器，繁琐且缺乏准确性，手术视觉正在发展成为一种使用内窥镜图像检测和跟踪器械的有前途的技术。本文综述了使用卷积神经网络（CNN）进行基于图像的腹腔镜工具检测和跟踪的文献，主要包括四个部分：（1）CNN 的基础知识；（2）公共数据集；（3）基于 CNN 的腹腔镜器械检测和跟踪方法；以及（4）讨论和结论。为了帮助研究人员快速了解各种现有的基于 CNN 的算法，我们从“部分 CNN 方法”和“全 CNN 方法”的角度分析和比较了一些基本信息和几个性能的定量估计。此外，我们强调了基于 CNN 的检测算法研究的相关挑战，并提供了可能的未来发展方向。
