  Deep learning-based methods have recently demonstrated promising results in
deformable image registration for a wide range of medical image analysis tasks.
However, existing deep learning-based methods are usually limited to small
deformation settings, and desirable properties of the transformation including
bijective mapping and topology preservation are often being ignored by these
approaches. In this paper, we propose a deep Laplacian Pyramid Image
Registration Network, which can solve the image registration optimization
problem in a coarse-to-fine fashion within the space of diffeomorphic maps.
Extensive quantitative and qualitative evaluations on two MR brain scan
datasets show that our method outperforms the existing methods by a significant
margin while maintaining desirable diffeomorphic properties and promising
registration speed.
