Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)
2026-07-14 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors organized a challenge in 2025 where different teams tested their computer programs designed to track points during surgery. These programs were checked for how accurately and quickly they could track points using a special dataset called STIR, which includes surgical images taken in infrared. The challenge helped compare different methods by looking at their performance on real and lab-based surgical videos. Seven teams joined, and the authors shared the results along with tools and data for others to use.
point trackingsurgical imaginginfrared imagingSTIR datasetalgorithm accuracyalgorithm efficiencyMICCAI EndoVis3D reconstructionvirtual tissue landmarkinginference latency
Authors
Adam Schmidt, Mert Asim Karaoglu, Zijian Wu, Jiaming Zhang, Yuxin Chen, Tim Salcudean, Ho-Gun Ha, Minkang Jang, Kyungmin Jung, Ihsan Ullah, Hyunki Lee, Suresh Guttikonda, Sarah Latus, Alexander Schlaefer, Xinkai Zhao, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori, Peng Liu, Chenyang Li, Stefanie Speidel, Aoife Gardiner, Agostino Stilli, Danail Stoyanov, Francisco Vasconcelos, Anwesa Choudhuri, Meng Zheng, Zhongpai Gao, Benjamin Planche, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu, Alexander Ladikos, Omid Mohareri
Abstract
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