  The crucial components of a conventional image registration method are the
choice of the right feature representations and similarity measures. These two
components, although elaborately designed, are somewhat handcrafted using human
knowledge. To this end, these two components are tackled in an end-to-end
manner via reinforcement learning in this work. Specifically, an artificial
agent, which is composed of a combined policy and value network, is trained to
adjust the moving image toward the right direction. We train this network using
an asynchronous reinforcement learning algorithm, where a customized reward
function is also leveraged to encourage robust image registration. This trained
network is further incorporated with a lookahead inference to improve the
registration capability. The advantage of this algorithm is fully demonstrated
by our superior performance on clinical MR and CT image pairs to other
state-of-the-art medical image registration methods.
