Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

中文翻译：

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CholecTriplet2021：外科动作三联体识别的基准挑战

通过利用来自手术工作流程分析的实时反馈，手术室中的情境感知决策支持可以提高手术的安全性和效率。大多数现有作品在粗粒度级别识别手术活动，例如阶段、步骤或事件，而忽略了有关手术活动的细粒度交互细节；然而，这些对于在手术室中提供更有用的 AI 帮助是必需的。将手术动作识别为三元组 组合提供有关手术视频中发生的活动的全面详细信息。本文介绍了 CholecTriplet2021：在 MICCAI 2021 上组织的内窥镜视觉挑战赛，用于识别腹腔镜视频中的手术动作三联体。该挑战授予了对大规模 CholecT50 数据集的私人访问权限，该数据集使用动作三元组信息进行注释。在本文中，我们介绍了参与者在挑战期间提出的最先进的深度学习方法的挑战设置和评估。挑战组织者提出了总共 4 种基线方法和竞争团队提出的 19 种新的深度学习算法，以直接从手术视频中识别手术动作三联体，平均精度 (mAP) 范围为 4.2% 至 38.1%。本研究还分析了所提出的方法获得的结果的重要性，对它们进行了彻底的方法学比较，深入的结果分析，并提出了一种新的集成方法来增强识别。我们的分析表明，手术工作流程分析尚未解决，也突出了未来研究细粒度手术活动识别的有趣方向，这对手术人工智能的发展至关重要。