Reuter, KonradKonradReuterGuttikonda, SureshSureshGuttikondaLatus, SarahSarahLatusMaack, LennartLennartMaackBetz, Christian StephanChristian StephanBetzMaurer, TobiasTobiasMaurerSchlaefer, AlexanderAlexanderSchlaefer2026-01-232026-01-232025-12-01Current Directions in Biomedical Engineering 11 (2): 96-99 (2025)https://hdl.handle.net/11420/61072Minimally invasive surgery presents challenges such as dynamic tissue motion and a limited field of view. Accurate tissue tracking has the potential to support surgical guidance, improve safety by helping avoid damage to sensitive structures, and enable context-aware robotic assistance during complex procedures. In this work, we propose a novel method for markerless 3D tissue tracking by leveraging 2D Tracking Any Point (TAP) networks. Our method combines two CoTracker models, one for temporal tracking and one for stereo matching, to estimate 3D motion from stereo endoscopic images. We evaluate the system using a clinical laparoscopic setup and a robotic arm simulating tissue motion, with experiments conducted on a synthetic 3D-printed phantom and a chicken tissue phantom. Tracking on the chicken tissue phantom yielded more reliable results, with Euclidean distance errors as low as 1.1 mm at a velocity of 10 mm/s. These findings highlight the potential of TAP-based models for accurate, markerless 3D tracking in challenging surgical scenarios.en2364-5504202529699Walter de Gruyter GmbHhttps://creativecommons.org/licenses/by/4.0/Technology::617: Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology::617.9: Operative Surgery and Special Fields of SurgeryTechnology::629: Other Branches::629.8: Control and Feedback Control Systems::629.89: Computer-Controlled Guidance::629.892: RobotComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.32: Neural NetworksJournal Articlehttps://doi.org/10.15480/882.1656610.1515/cdbme-2025-032510.15480/882.16566Journal Article