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Reconstructing a 3D heart surface with stereo-endoscope by learning eigen-shapes

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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02421182
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Bo Yang, Chao Liu, Wenfeng Zheng, Shan Liu, Keli Huang. Reconstructing a 3D heart surface with stereo-endoscope by learning eigen-shapes. Biomedical optics express, Optical Society - SOA Publishing, 2018, 9 (12), pp.6222-6236. ⟨10.1364/BOE.9.006222⟩. ⟨lirmm-02421182⟩

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