Image registration is an important component for many medical
image analysis methods. It is a means to establish spatial
correspondences within or across subjects. Non-parametric registration
approaches typically involve the solution of very high-dimensional
numerical optimization problems. However, more recently, deep learning
approaches have been proposed that replace numerical optimization by
regression. These approaches are therefore very fast at test time. In
this talk I will give an overview of some work on deep-learning
approaches for medical image registration, and, in particular, will talk
about some recent work on metric estimation for image registration.
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