PCA-based 3D Pose Modeling for Beating Heart Tracking

International audienceA 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 principalcomponent-based dimensionality reduction. The proposed 3D pose modeling is validated on the stereo-endoscopic videos recorded by the da Vinci® surgical system