Deep Similarity Learning Using A Siamese Resnet Trained On Similarity Labels From Disparity Maps Of Cerebral Mra Mip Pairs

Similarity measures are a critical component for iterative registration tasks. In intrinsic 2D/3D registration an initialization close to the solution is usually needed for convergence. Traditional similarity measures are limited in their applicability to perspective projection data. Deep Learning allows us to create similarity measures that encode almost arbitrary non-linear relationships like perspective projection. We apply a siamese network and a 2-channel network to the problem of comparing two perspective maximum intensity projections of medical data. We propose the use of residual units as the building block. A major challenge is to define similarity under perspective projection so that it can be used for training. Extrinsic registration gives us a gold standard of registered examples but we need a way to quantify the dissimilarity of unregistered image pairs for training. We propose the use of disparity as the basis for construction of similarity labels, i.e. labels are based on distances between projected points when viewing the same 3D point through different cameras. The final similarity labels are independent of the choice of camera parametrization and describe the visual similarity of two given views of a common 3D model. We compare our deep similarity metric to traditional similarity measures for a comparative evaluation. Our best configuration has a Pearson correlation coefficient of 0:792 and a Spearman's rank correlation coefficient of 0:480. The best traditional method is normalized cross correlation with a 5:4% lower Spearman's rank correlation coefficient.