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次医学图像计算和计算机辅助干预国际会议(MICCAI 2022)共同组织的“超标:外科跟踪”挑战,提出挑战有两个目的:(1) 为研究界建立第一个标准基准,以评估软质跟踪器;(2) 由于缺乏附加说明的数据,鼓励开发不受监督的深层次学习方法;提供了20个临床病例157个立体内窥镜视频数据集,以及立体相机校准参数;与会者的任务是开发算法,以跟踪立体内镜视频的捆绑盒;在挑战结束时,对开发的方法进行了先前隐蔽的测试子集进行评估;评估使用了专门为这一挑战制定的基准度指标,现在可以在线查阅;小组的排名与其预期平均重叠(EAO)分数(这是Interdection(IoU)分数的加权平均数; 业绩评估研究核实了未超超前的内科外科手术技术的功效; 在软体格内科测试中,这种预期的内科外科手术技术的功效,是用于跟踪可获取的内科的内科。</s>