  Conventional approaches to image registration consist of time consuming
iterative methods. Most current deep learning (DL) based registration methods
extract deep features to use in an iterative setting. We propose an end-to-end
DL method for registering multimodal images. Our approach uses generative
adversarial networks (GANs) that eliminates the need for time consuming
iterative methods, and directly generates the registered image with the
deformation field. Appropriate constraints in the GAN cost function produce
accurately registered images in less than a second. Experiments demonstrate
their accuracy for multimodal retinal and cardiac MR image registration.
