PCA-based 3D pose modeling for beating heart tracking

A statistical pose model is developed for efficient 3D visual tracking of beating heart. The Region of Interest (ROI) on heart surfaces is first pre-tracked with a conventional high-order thin plate spline model. The 3D pose data of the ROI extracted from the pre-tracked results are then used to train a low-order 3D pose model based on the principal component of these pose data. The low-order model is accurate, robust, and efficient for tracking subsequent heart motion as heart beats are quasi-periodic with stable statistics and the redundant degree of freedom for fitting the poses of heart surface is significantly decreased by the principal-component-based dimensionality reduction. The proposed 3D pose modeling is validated on the stereo-endoscopic videos recorded by the da Vinci® surgical system.

[1]  Gregory D. Hager,et al.  Stereo-Based Endoscopic Tracking of Cardiac Surface Deformation , 2004, MICCAI.

[2]  Guang-Zhong Yang,et al.  Three-Dimensional Tissue Deformation Recovery and Tracking , 2010, IEEE Signal Processing Magazine.

[3]  Guang-Zhong Yang,et al.  Real-Time Stereo Reconstruction in Robotically Assisted Minimally Invasive Surgery , 2010, MICCAI.

[4]  Tobias Ortmaier,et al.  Motion estimation in beating heart surgery , 2005, IEEE Transactions on Biomedical Engineering.

[5]  Danail Stoyanov,et al.  A practical approach towards accurate dense 3D depth recovery for robotic laparoscopic surgery , 2005, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[6]  Philippe Poignet,et al.  3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model , 2014, Pattern Recognit..

[7]  Uwe D. Hanebeck,et al.  Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems , 2011, International Journal of Computer Assisted Radiology and Surgery.

[8]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .

[9]  Yoshihiko Nakamura,et al.  Heartbeat synchronization for robotic cardiac surgery , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[10]  Guang-Zhong Yang,et al.  Dynamic Guidance for Robotic Surgery Using Image-Constrained Biomechanical Models , 2010, MICCAI.

[11]  Selim Benhimane,et al.  Homography-based 2D Visual Tracking and Servoing , 2007, Int. J. Robotics Res..

[12]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[13]  Chao Liu,et al.  Three-dimensional Motion Tracking for Beating Heart Surgery Using a Thin-plate Spline Deformable Model , 2010, Int. J. Robotics Res..

[14]  Robert D. Howe,et al.  Robotic Motion Compensation for Beating Heart Intracardiac Surgery , 2009, Int. J. Robotics Res..

[15]  Chao Liu,et al.  A triangular radial cubic spline deformation model for efficient 3D beating heart tracking , 2017, Signal Image Video Process..

[16]  Uwe D. Hanebeck,et al.  Model-based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[17]  Guang-Zhong Yang,et al.  Soft-Tissue Motion Tracking and Structure Estimation for Robotic Assisted MIS Procedures , 2005, MICCAI.