Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems. Nowadays, learning-based action recognition and segmentation approaches outperform classical methods, relying, however, on large, annotated datasets. Furthermore, action recognition and tool segmentation algorithms are often trained and make predictions in isolation from each other, without exploiting potential cross-task relationships. With the EndoVis 2022 SAR-RARP50 challenge, we release the first multimodal, publicly available, in-vivo, dataset for surgical action recognition and semantic instrumentation segmentation, containing 50 suturing video segments of Robotic Assisted Radical Prostatectomy (RARP). The aim of the challenge is twofold. First, to enable researchers to leverage the scale of the provided dataset and develop robust and highly accurate single-task action recognition and tool segmentation approaches in the surgical domain. Second, to further explore the potential of multitask-based learning approaches and determine their comparative advantage against their single-task counterparts. A total of 12 teams participated in the challenge, contributing 7 action recognition methods, 9 instrument segmentation techniques, and 4 multitask approaches that integrated both action recognition and instrument segmentation. The complete SAR-RARP50 dataset is available at: https://rdr.ucl.ac.uk/projects/SARRARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091

翻译：手术器械分割与动作识别是许多计算机辅助干预应用（从手术技能评估到决策支持系统）的基础构建模块。当前，基于学习的动作识别与分割方法虽优于传统方法，但依赖大规模标注数据集。此外，动作识别与器械分割算法常彼此孤立地训练和预测，未能利用跨任务潜在关联。通过EndoVis 2022 SAR-RARP50挑战，我们发布了首个多模态、公开可用的体内手术动作识别与语义器械分割数据集，包含50段机器人辅助根治性前列腺切除术（RARP）缝合视频片段。该挑战旨在实现双重目标：第一，使研究者能够利用所提供数据集的规模，开发手术领域鲁棒且高精度的单任务动作识别与器械分割方法；第二，进一步探索基于多任务学习方法的潜力，并确定其相较于单任务方法的相对优势。共有12支团队参与挑战，贡献了7种动作识别方法、9种器械分割技术，以及4种整合动作识别与器械分割的多任务方法。完整SAR-RARP50数据集可通过以下链接获取：https://rdr.ucl.ac.uk/projects/SARRARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091