This paper introduces the SurgT MICCAI 2022 challenge and its first results. There were two purposes for the creation of this challenge: (1) the establishment of the first standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, are provided. The participants were tasked with the development of algorithms to track a bounding box on each stereo endoscopic video. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge and are now available online. The teams were ranked according to their Expected Average Overlap (EAO) score, which is a weighted average of Intersection over Union (IoU) scores. The top team achieved an EAO score of 0.583 in the test subset. Tracking soft-tissue using unsupervised algorithms was found to be achievable. The dataset and benchmarking tool have been successfully created and made publicly available online. This challenge is expected to contribute to the development of autonomous robotic surgery, and other digital surgical technologies.

翻译：本文介绍了SurgT MICCAI 2022挑战赛及其初步结果。设立该挑战有两重目的：（1）为研究社区建立首个标准化基准测试，以评估软组织追踪器；（2）鉴于手术场景中标注数据的匮乏，鼓励无监督深度学习方法的研发。挑战赛提供了来自20个临床病例的157段立体内窥镜视频，以及立体相机标定参数。参与者需开发算法，在每段立体内窥镜视频中追踪一个边界框。挑战赛结束后，各团队开发的方法在先前未公开的测试子集上进行了评估。该评估使用了专门为本次挑战开发的基准测试指标，这些指标现已在线公开。团队根据其预期平均重叠（EAO）得分进行排名，该得分是交并比（IoU）分数的加权平均值。排名第一的团队在测试子集上取得了0.583的EAO得分。研究发现，使用无监督算法实现软组织追踪是可行的。数据集与基准测试工具已成功创建并在线公开。预计本次挑战将推动自主机器人手术及其他数字外科技术的发展。