In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.

翻译：在计算机与机器人辅助微创手术领域，基于内窥镜图像与视频中手术器械识别的研究近年来取得了巨大进展。其中，器械位置与类型的确定尤为关键。当前工作同时利用空间与时间信息，其核心理念在于通过预测手术器械随时间变化的运动轨迹，可提升最终分割质量。公开可用数据集的提供，近期极大促进了以深度学习为主的新方法开发。本综述系统识别并刻画了用于方法开发与评估的数据集特征，量化了其在文献中的使用频率。我们进一步概述了内窥镜图像与视频中微创手术器械分割与追踪的研究前沿。本文聚焦于纯视觉方法（不依赖器械附着任何标记），涵盖单帧语义分割与实例分割方法，以及融合时间信息的方法。分析文献通过Google Scholar、Web of Science及PubMed平台检索，采用"器械分割""器械追踪""手术工具分割""手术工具追踪"作为检索词，共获得2015年1月至2023年7月间发表的741篇文章，经系统筛选标准纳入123篇。本文对综述文献展开讨论，指出现存缺陷并强调未来发展的潜在空间。