SARAS内窥镜外科医生动作检测（ESAD）数据集：挑战和方法
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
摘要：
对于自主机器人系统，在手术过程中监测外科医生的动作并协助主要外科医生可能非常具有挑战性。挑战来自手术场景的特殊结构，与不受约束的环境中的人类动作相比，通过工具在腔中执行的动作的外观更相似，以及来自内窥镜相机的运动。本文介绍了ESAD，第一个大规模的数据集，旨在解决内窥镜微创手术中的外科医生动作检测问题。ESAD旨在通过真实地测试手术辅助机器人对外科医生所执行动作的意识，帮助提高手术辅助机器人的有效性和可靠性。该数据集为在椎间盘切除术期间捕获的真实内窥镜视频帧上的21个动作类提供了限界框注释，并用作最近MIDL 2020挑战的基础。我们还介绍了使用作为挑战的一部分发布的基线模型进行的数据集分析，以及提交给挑战的最佳性能模型及其获得的结果的描述。这项研究提供了重要的见解，什么样的方法可以是有效的，可以进一步推广。我们相信，ESAD将在未来作为一个有用的基准，所有研究人员积极在外科医生的行动检测和辅助机器人在大。
英文摘要：
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar structure of the surgical scene, the greater similarity in appearance of actions performed via tools in a cavity compared to, say, human actions in unconstrained environments, as well as from the motion of the endoscopic camera. This paper presents ESAD, the first large-scale dataset designed to tackle the problem of surgeon action detection in endoscopic minimally invasive surgery. ESAD aims at contributing to increase the effectiveness and reliability of surgical assistant robots by realistically testing their awareness of the actions performed by a surgeon. The dataset provides bounding box annotation for 21 action classes on real endoscopic video frames captured during prostatectomy, and was used as the basis of a recent MIDL 2020 challenge. We also present an analysis of the dataset conducted using the baseline model which was released as part of the challenge, and a description of the top performing models submitted to the challenge together with the results they obtained. This study provides significant insight into what approaches can be effective and can be extended further. We believe that ESAD will serve in the future as a useful benchmark for all researchers active in surgeon action detection and assistive robotics at large.
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。
