Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
This improves rigid registration for minimally invasive medical procedures, but it is incremental as it builds on existing correspondence-based methods.
The paper tackles the problem of inaccurate correspondences in 2-D/3-D medical image registration by learning optimal weights for correspondences using PointNet, achieving an accuracy of 0.74±0.26 mm and increasing success rate from 79.3% to 94.3%.
Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of 0.74$\pm$0.26 mm and highly improved robustness. The success rate is increased from 79.3 % to 94.3 % and the capture range from 3 mm to 13 mm.