Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
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Registration for pre-operative 3-D images to intra-operative 2-D fluoroscopy images is important in minimally invasive procedures. Registration can be intuitively performed by estimating the global rigidbody motion with constraints of minimizing local misalignments. However, inaccurate local correspondences challenge the registration performance. We use PointNet to estimate the optimal weights of local correspondences. We train the network directly with the criterion to minimize the registration error. For that, we propose an objective function which incorporates point-to-plane motion estimation and projection error computation. Thereby, we enable the learning of a correspondence weighting strategy which optimally fits the underlying formulation of the registration problem in an end-to-end fashion. In the evaluation of single-vertebra registration, we demonstrate an accuracy of 0.74±0.26 mm of our method and a highly improved robustness, increasing the success rate from 79.3
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