Abstract
In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking is a difficult task. The purpose of this paper is to present a hybrid tracker that estimates the heart surface deformation using the outputs of multiple visual tracking techniques. In the proposed method, the failure of an individual technique can be circumvented by the success of others, enabling the robust estimation of the heart surface deformation with increased spatial resolution. In addition, for coping with the absence of visual information due to motion blur or occlusions, a temporal heart motion model is incorporated as an additional support for the visual tracking task. The superior performance of the proposed technique compared to existing techniques individually is demonstrated through experiments conducted on recorded images of an in vivo minimally invasive CABG using the DaVinci robotic platform.
Additional material can be found at http://www.lirmm.fr/~richa/my_files/video_miccai10.flv and http://www.lirmm.fr/~richa/my_files/evaluating_tracking_quality.pdf .
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Nakamura, Y.K., Kawakami, H.: Heartbeat synchronization for robotic cardiac surgery. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2001), Seoul, Korea, vol. 2, pp. 2014–2019 (May 2001)
Stoyanov, D., Mylonas, G.P., Deligianni, F., Darzi, A., Yang, G.Z.: Soft-tissue motion tracking and structure estimation for robotic assisted mis procedures. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 139–146. Springer, Heidelberg (2005)
Mountney, P., Yang, G.Z.: Soft tissue tracking for minimally invasive surgery: Learning local deformation online. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 364–372. Springer, Heidelberg (2008)
Richa, R., Poignet, P., Liu, C.: Three-dimensional motion tracking for beating heart surgery using a thin-plate spline deformable model. The International Journal of Robotics Research (IJRR) – Special Issue on Robot Vision 29(2-3), 218–230 (2010)
Visentini-Scarzanella, M., Mylonas, G.P., Stoyanov, D., Yang, G.Z.: i-BRUSH: A Gaze-Contigent Virtual Paintbrush for Dense 3D Reconstruction in Robotic Assisted Surgery. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 353–360. Springer, Heidelberg (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Stoyanov, D., Yang, G.Z.: Soft-tissue deformation tracking for robotic assisted minimally invasive surgery. In: Proceedings of IEEE International Conference of the Engineering in Medicine and Biology Society (EMBS 2009), Boston, USA, pp. 254–257 (2009)
Bogatyrenko, E., Hanebeck, U.D., Szabó, G.: Heart surface motion estimation framework for robotic surgery employing meshless methods. In: Proceedings of IEEE Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, USA, pp. 67–74 (2009)
Richa, R., Bó, A.P.L., Poignet, P.: Beating heart motion prediction for robust visual tracking. In: Proceedings of IEEE Conference on Robotics and Automation (ICRA 2010), Anchorage, USA, pp. 4579–4584 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Richa, R., Bó, A.P.L., Poignet, P. (2010). Robust 3D Visual Tracking for Robotic-Assisted Cardiac Interventions. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-15705-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
Online ISBN: 978-3-642-15705-9
eBook Packages: Computer ScienceComputer Science (R0)