Understanding tissue motion 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. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 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 the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here:this https URL, and the code for baseline models and metric calculation is available here:this https URL
View on arXiv@article{schmidt2025_2503.24306,
title={ Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge },
author={ Adam Schmidt and Mert Asim Karaoglu and Soham Sinha and Mingang Jang and Ho-Gun Ha and Kyungmin Jung and Kyeongmo Gu and Ihsan Ullah and Hyunki Lee and Jonáš Šerých and Michal Neoral and Jiří Matas and Rulin Zhou and Wenlong He and An Wang and Hongliang Ren and Bruno Silva and Sandro Queirós and Estêvão Lima and João L. Vilaça and Shunsuke Kikuchi and Atsushi Kouno and Hiroki Matsuzaki and Tongtong Li and Yulu Chen and Ling Li and Xiang Ma and Xiaojian Li and Mona Sheikh Zeinoddin and Xu Wang and Zafer Tandogdu and Greg Shaw and Evangelos Mazomenos and Danail Stoyanov and Yuxin Chen and Zijian Wu and Alexander Ladikos and Simon DiMaio and Septimiu E. Salcudean and Omid Mohareri },
journal={arXiv preprint arXiv:2503.24306},
year={ 2025 }
}