  3-D image registration, which involves aligning two or more images, is a
critical step in a variety of medical applications from diagnosis to therapy.
Image registration is commonly performed by optimizing an image matching metric
as a cost function. However, this task is challenging due to the non-convex
nature of the matching metric over the plausible registration parameter space
and insufficient approaches for a robust optimization. As a result, current
approaches are often customized to a specific problem and sensitive to image
quality and artifacts. In this paper, we propose a completely different
approach to image registration, inspired by how experts perform the task. We
first cast the image registration problem as a "strategy learning" process,
where the goal is to find the best sequence of motion actions (e.g. up, down,
etc.) that yields image alignment. Within this approach, an artificial agent is
learned, modeled using deep convolutional neural networks, with 3D raw image
data as the input, and the next optimal action as the output. To cope with the
dimensionality of the problem, we propose a greedy supervised approach for an
end-to-end training, coupled with attention-driven hierarchical strategy. The
resulting registration approach inherently encodes both a data-driven matching
metric and an optimal registration strategy (policy). We demonstrate, on two
3-D/3-D medical image registration examples with drastically different nature
of challenges, that the artificial agent outperforms several state-of-art
registration methods by a large margin in terms of both accuracy and
robustness.
