Motion Tracking for Beating Heart Based on Sparse Statistic Pose Modeling.

Abstract

A novel region-based method to track beating heart is proposed. Sparse statistical pose modeling is used to reconstruct the region of interest (ROI) on beating heart surface. Firstly, a high-complexity thin plate spline is employed to pre-reconstructed the ROI of a series of frames. The 3D pose data of the ROI from the pre-reconstructed results are extracted to train a low-complexity model based on the sparse statistical analysis. The new trained low-complexity model is robust and efficient for ROI reconstruction of the following frames. The proposed model significantly reduces the redundant degrees of freedom to fit the surface of the heart. A constraint item is added to the objective function which describes the 3D tracking problem to avoid erroneous convergence of the efficient second-order minimization (ESM) optimization algorithm. The new proposed method is evaluated on the phantom heart video and the in vivo video obtained by the da Vinci surgical system.