[Deformable Registration Network Reinforced by Joint Saliency Map]

Image outliers such as missing correspondences and large local deformations break the one-to-one pixelwise mapping between target image and moving image to be registered. Both traditional registration methods and deep-learning based deformable image registration methods fail to tackle this problem. This paper proposed an unsupervised globalto-local deformable registration network reinforced by joint saliency map to accurately, robustly and fast address the problem. The global-to-local network divided the overall learning of a complex mapping of image registration into a simpler global mapping learning and local residual mapping. The joint saliency map of the two images to be registered bidirectionally reinforced the whole network's forward estimation and back-propagation with uncertainty modeling and context-aware intelligence. The experimental results confirm the proposed method's performance advantages over the state-of-the-arts registration methods in the challenges image registration with missing correspondences and large local deformations.