Investigating the trackability of landmarks in NBI and WLI
Authors: Jonas Below, Piermarco Gerli, Anna Jötten, Dennis Eggert, Axelle Felicio-Briegel, Veronika Volgger, Christian Betz, Alexander Schlaefer
Organizations: Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Hamburg, Germany · Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, Germany · LMU Klinikum, Marchioninistr. 15, 81377 München, Germany
Reliable visual tracking of anatomical landmarks in endoscopic video is a key prerequisite for computer-assisted interventions in laryngeal surgery. While Narrow Band Imaging (NBI) is clinically established for enhancing vascular contrast, its effect on automated tracking relative to conventional White Light Imaging (WLI) remains unexplored. We present a quantitative comparison of landmark trackability in NBI and WLI using a forward-backward error framework applied to 61 endoscopic videos from 39 patients. Two regions of interest are evaluated: the vocal folds (VF) and mucosa with a prominent vascular pattern (MPVP). We compare a classical feature-based tracker (CSRT) against learning-based point trackers (CoTracker3-Online and TrackOnR) across both modalities. NBI consistently reduced the forward-backward error in both regions, reaching statistical significance for the vocal folds, while failure rates stayed low. These findings indicate that NBI can improve tracking robustness in laryngeal endoscopy, with implications for motion analysis, navigation, and robotic assistance.
Figures & tables
Fig. 1: Example placement of n~=100 tracking candidates within manually annotated VF (red) and MPVP ROIs (green). For point-based trackers, the centroid of each box was used as the tracking point.
Tab. 1: mFBE in pixels ( ↓ ) for MPVP and VF, including the p-value of the modality comparison.
Point tracking follows selected or dense image points through video, enabling motion analysis, 3D reconstruction, and precise spatiotemporal understanding.
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, P.R. China · Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510000, P.R. China · School of Computer Science and Technology, Hainan University, Haikou, 570228, China +2
Department of Electrical, Information and Bioengineering Polytechnic University of Milan Milan, Italy · Department of of Electrical, Information and Bioengineering Polytechnic University of Milan Milan, Italy