Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
This addresses robust image registration for medical imaging, though it appears incremental as it builds on existing reinforcement learning approaches.
The paper tackles the problem of multimodal image registration by training an agent with reinforcement learning to adjust moving images, achieving superior performance on clinical MR and CT image pairs compared to state-of-the-art methods.
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.