Abstract
An efficient approach to dynamically reconstruct a region of interest (ROI) on a beating heart from stereo-endoscopic video is developed. A ROI is first pre-reconstructed with a decoupled high-rank thin plate spline model. Eigen-shapes are learned from the pre-reconstructed data by using principal component analysis (PCA) to build a low-rank statistical deformable model for reconstructing subsequent frames. The linear transferability of PCA is proved, which allows fast eigen-shape learning. A general dynamic reconstruction framework is developed that formulates ROI reconstruction as an optimization problem of model parameters, and an efficient second-order minimization algorithm is derived to iteratively solve it. The performance of the proposed method is finally validated on stereo-endoscopic videos recorded by da Vinci robots.
Highlights
In robotic-assisted off-pump heart surgery, heart beating considerably influences the accuracy of surgical operations, resulting in longer surgical duration and increased surgical risks
We propose a statistical modeling method to reconstruct region of interest (ROI) on beating heart from stereoendoscopic video
Since the statistical model is trained specially for fitting the 3D structure of the ROI, it is very efficient when incorporated into a model-based reconstruction framework, which formulates the dynamic reconstruction as an optimization problem of model parameters for each stereo image frame
Summary
In robotic-assisted off-pump heart surgery, heart beating considerably influences the accuracy of surgical operations, resulting in longer surgical duration and increased surgical risks. The 3D reconstruction with endoscope is important for other advanced surgical techniques, e.g. augmented reality guidance [7], and multispectral [8] or multimodal [9] imaging. It is useful for offline applications as well, such as surgery simulation [10] and visual medical record [11], for learning, training or evaluation purposes. In a highly dynamic MIS, it is very challenging to track and reconstruct Regions of Interest (ROIs) with complex soft-tissue deformations from real-time endoscopic videos. Low-rank models, such as rigid and affine models, are robust and computationally efficient but difficult to deal with complex soft-tissue deformations, while high-rank models generally suffer from problems of parameter convergence and heavy computational burden, difficult to meet real-time and robustness requirements, as indicated in [16]
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