This paper introduces the "SurgT: Surgical Tracking" challenge which was organised in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). 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, have been provided. The participants were tasked with the development of algorithms to track a bounding box on stereo endoscopic videos. 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 the Intersection over Union (IoU) scores. The performance evaluation study verifies the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The best-performing method achieved an EAO score of 0.583 in the test subset. The dataset and benchmarking tool created for this challenge have been made publicly available. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

翻译：本文介绍了与第25届国际医学图像计算与计算机辅助干预大会（MICCAI 2022）同期举办的"SurgT：手术跟踪"挑战赛。设立该挑战赛有两个目的：（1）为研究社区建立首个标准化基准，用于评估软组织跟踪器；（2）鉴于手术领域标注数据的匮乏，鼓励无监督深度学习方法的开发。研究团队提供了来自20个临床病例的157个立体内窥镜视频数据集，以及立体相机标定参数。参赛者需开发算法，在立体内窥镜视频中跟踪边界框。挑战赛结束后，所有开发的方法均在先前隐藏的测试子集上进行了评估。评估采用了专为本次挑战开发的基准度量指标，目前这些指标已公开发布。各参赛队伍根据预期平均重叠（EAO）分数进行排名，该分数是交并比（IoU）分数的加权平均值。性能评估研究验证了无监督深度学习算法在软组织跟踪中的有效性。最佳方法在测试子集上获得了0.583的EAO分数。本次挑战赛创建的数据集和基准评估工具均已公开提供。该挑战赛有望为自主机器人手术及其他数字手术技术的发展做出贡献。