Endoscopic feature tracking for augmented-reality assisted prosthesis selection in mitral valve repair

Sandy Engelhardt; Silvio Kolb; Raffaele De Simone; Matthias Karck; Hans-Peter Meinzer; Ivo Wolf

doi:10.1117/12.2216239

18 March 2016 Endoscopic feature tracking for augmented-reality assisted prosthesis selection in mitral valve repair

Sandy Engelhardt, Silvio Kolb, Raffaele De Simone, Matthias Karck, Hans-Peter Meinzer, Ivo Wolf

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 97861A (2016) https://doi.org/10.1117/12.2216239
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Mitral valve annuloplasty describes a surgical procedure where an artificial prosthesis is sutured onto the anatomical structure of the mitral annulus to re-establish the valve's functionality. Choosing an appropriate commercially available ring size and shape is a difficult decision the surgeon has to make intraoperatively according to his experience. In our augmented-reality framework, digitalized ring models are superimposed onto endoscopic image streams without using any additional hardware. To place the ring model on the proper position within the endoscopic image plane, a pose estimation is performed that depends on the localization of sutures placed by the surgeon around the leaflet origins and punctured through the stiffer structure of the annulus. In this work, the tissue penetration points are tracked by the real-time capable Lucas Kanade optical flow algorithm. The accuracy and robustness of this tracking algorithm is investigated with respect to the question whether outliers influence the subsequent pose estimation. Our results suggest that optical flow is very stable for a variety of different endoscopic scenes and tracking errors do not affect the position of the superimposed virtual objects in the scene, making this approach a viable candidate for annuloplasty augmented reality-enhanced decision support.

Citation Download Citation

Sandy Engelhardt, Silvio Kolb, Raffaele De Simone, Matthias Karck, Hans-Peter Meinzer, and Ivo Wolf "Endoscopic feature tracking for augmented-reality assisted prosthesis selection in mitral valve repair", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97861A (18 March 2016); https://doi.org/10.1117/12.2216239

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