  Registration is an important task in automated medical image analysis.
Although deep learning (DL) based image registration methods out perform time
consuming conventional approaches, they are heavily dependent on training data
and do not generalize well for new images types. We present a DL based approach
that can register an image pair which is different from the training images.
This is achieved by training generative adversarial networks (GANs) in
combination with segmentation information and transfer learning. Experiments on
chest Xray and brain MR images show that our method gives better registration
performance over conventional methods.
