Learning-based Hypothesis Fusion for Robust Catheter Tracking in 2D X-ray Fluoroscopy

Catheter tracking has become more and more important in recent interventional applications. It provides real time guidance for the physicians and can be used as motion compensated reference for other guidance, e.g. a 3D anatomical model. Tracking the coronary sinus (CS) catheter is effective to compensate respiratory and cardiac motion for 3D overlay to assist positioning the ablation catheter in Atrial Fibrillation (Afib) treatments. During interventions, the CS catheter performs rapid motion and non-rigid deformation due to the beating heart and respiration. In this paper, we model the CS catheter as a set of electrodes. Novelly designed hypotheses generated by a number of learning-based detectors are fused. Robust hypothesis matching through a Bayesian framework is then used to select the best hypothesis for each frame. As a result, the tracking achieves very high robustness against challenging scenarios such as low SNR, occlusion, foreshortening, non-rigid deformation, as well as...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Wen Wu, Terrence Chen, Adrian Barbu, Peng Wang, Norbert Strobel, Shaohua Zhou, Comaniciu Dorin
• Keywords: Ablation Catheters, Catheters, Computer Vision, CVPR 2011, Non-rigid Deformation