Agent-Based Methods For Medical Image Registration

Medical imaging registration is a critical step in a wide spectrum of medical applications from diagnosis to therapy and has been an extensively studied research field. Prior to the popularity of deep learning, image registration was commonly performed by optimizing an image matching metric as a cost function in search for the optimal registration. However, the optimization task is known to be challenging due to (1) the non-convex nature of the matching metric over the registration parameter space and (2) the lack of effective approaches for robust optimization. With the latest advance in deep learning and artificial intelligence, the field of medical image registration had a major paradigm shift, whereby learning-based image registration methods are developed to employ deep neural networks to analyze images in order to estimate plausible registrations. Among the latest advances in learning-based registration methods, agent-based methods have been shown to be effective in both 3-D/3-D and 2-D/3-D registrations with significant robustness advantage over conventional optimization-based methods. In this chapter, we give an overview of agent-based methods for medical image registration and its two applications on rigid-body 3-D/3-D and 2-D/3-D registrations.