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Journal articles
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.
Cited literature [33 references]
https://hal-lirmm.ccsd.cnrs.fr/lirmm-02421182
Contributor : Isabelle Gouat <>
Submitted on : Friday, December 20, 2019 - 12:13:40 PM
Last modification on : Saturday, December 21, 2019 - 1:38:56 AM
Document(s) archivé(s) le : Saturday, March 21, 2020 - 7:24:31 PM
Contributor : Isabelle Gouat <>
Submitted on : Friday, December 20, 2019 - 12:13:40 PM
Last modification on : Saturday, December 21, 2019 - 1:38:56 AM
Document(s) archivé(s) le : Saturday, March 21, 2020 - 7:24:31 PM
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boe-9-12-6222.pdf
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