  We present a novel multilevel approach for deep learning based image
registration. Recently published deep learning based registration methods have
shown promising results for a wide range of tasks. However, these algorithms
are still limited to relatively small deformations. Our method addresses this
shortcoming by introducing a multilevel framework, which computes deformation
fields on different scales, similar to conventional methods. Thereby, a
coarse-level alignment is obtained first, which is subsequently improved on
finer levels. We demonstrate our method on the complex task of inhale-to-exhale
lung registration. We show that the use of a deep learning multilevel approach
leads to significantly better registration results.
