发表机构
Intuitive Surgical Inc.; ImFusion GmbH; Technical University of Munich; University of British Columbia; Johns Hopkins University; Daegu Gyeongbuk Institute of Science and Technology; Hamburg University of Technology; SustAInLivWork Center of Excellence; Graduate School of Informatics, Nagoya University; Information Technology Center, Nagoya University; Department of Information Science, Aichi Institute of Technology; Research Center for Medical Bigdata, National Institute of Informatics; Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresde（直观外科公司; ImFusion有限公司; 慕尼黑工业大学; 英属哥伦比亚大学; 约翰·霍普金斯大学; 大邱庆北科学技术院; 汉堡工业大学; 可持续生活工作卓越中心; 名古屋大学信息科学研究生院; 名古屋大学信息技术中心; 爱知工业大学信息科学系; 国立信息学研究所医学大数据研究中心; 德累斯顿国家肿瘤疾病中心（NCT）转化外科肿瘤学系，NCT/UCC德累斯顿，由德国癌症研究中心、医学院和卡尔·古斯塔夫·卡鲁斯大学医院、德累斯顿工业大学合作成立）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍2025年手术点跟踪挑战（STIRC2025），参与者提交算法基于红外手术纹身数据集（STIR）评估，含准确性和效率两组件，作为MICCAI EndoVis 2025一部分进行，总结结果与方法，数据集和代码可获取。
AI 中文摘要
手术中的点跟踪对于实现诸如分割、3D重建、虚拟组织地标定位、基于自主探头的扫描和子任务自主等下游任务至关重要。本文介绍了点跟踪挑战的2025年迭代，参与者提交算法进行量化。使用名为红外手术纹身（STIR）的数据集评估算法，该挑战称为2025年STIR挑战（STIRC2025）。STIRC2025包括准确性和效率两个定量组件。准确性组件测试算法在体内和体外序列上的准确性，效率组件测试算法推理延迟。该挑战作为MICCAI EndoVis 2025的一部分进行，七个团队参与。本文总结了挑战结果和参与者方法。挑战数据集和基线模型及指标计算代码可通过链接获取。
英文摘要
Point tracking in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. This paper introduces the 2025 iteration of a point tracking challenge to address this, wherein participants submit their algorithms for quantification. Their algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge named the STIR Challenge 2025 (STIRC2025). The STIR Challenge 2025 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests algorithm inference latency. The challenge was conducted as a part of MICCAI EndoVis 2025, and seven teams participated in this challenge. In this paper we summarize the challenge results and participant methods. The challenge dataset is available at: https://zenodo.org/records/20191078, and the code for baseline models and metrics calculation is available here: https://github.com/athaddius/STIRMetrics
Comments9 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2503.24306
