Robust Tracking with Particle Filtering for Fluorescent Cardiac Imaging
Abstract
Intraoperative fluorescent cardiac imaging enables quality control following coronary bypass grafting surgery. We can estimate local quantitative indicators, such as cardiac perfusion, by tracking local feature points. However, heart motion and significant fluctuations in image characteristics caused by vessel structural enrichment limit traditional tracking methods. We propose a particle filtering tracker based on cyclicconsistency checks to robustly track particles sampled to follow target landmarks. Our method tracks 117 targets simultaneously at 25.4 fps, allowing real-time estimates during interventions. It achieves a tracking error of (5.00 +/- 0.22 px) and outperforms other deep learning trackers (22.3 +/- 1.1 px) and conventional trackers (58.1 +/- 27.1 px).
Cite
@article{arxiv.2508.05262,
title = {Robust Tracking with Particle Filtering for Fluorescent Cardiac Imaging},
author = {Suresh Guttikonda and Maximilian Neidhart and Johanna Sprenger and Johannes Petersen and Christian Detter and Alexander Schlaefer},
journal= {arXiv preprint arXiv:2508.05262},
year = {2025}
}
Comments
Accepted to CURAC conference 2025