  Conventional deformable registration methods aim at solving an optimization
model carefully designed on image pairs and their computational costs are
exceptionally high. In contrast, recent deep learning based approaches can
provide fast deformation estimation. These heuristic network architectures are
fully data-driven and thus lack explicit geometric constraints, e.g.,
topology-preserving, which are indispensable to generate plausible
deformations. We design a new deep learning based framework to optimize a
diffeomorphic model via multi-scale propagation in order to integrate
advantages and avoid limitations of these two categories of approaches.
Specifically, we introduce a generic optimization model to formulate
diffeomorphic registration and develop a series of learnable architectures to
obtain propagative updating in the coarse-to-fine feature space. Moreover, we
propose a novel bilevel self-tuned training strategy, allowing efficient search
of task-specific hyper-parameters. This training strategy increases the
flexibility to various types of data while reduces computational and human
burdens. We conduct two groups of image registration experiments on 3D volume
datasets including image-to-atlas registration on brain MRI data and
image-to-image registration on liver CT data. Extensive results demonstrate the
state-of-the-art performance of the proposed method with diffeomorphic
guarantee and extreme efficiency. We also apply our framework to challenging
multi-modal image registration, and investigate how our registration to support
the down-streaming tasks for medical image analysis including multi-modal
fusion and image segmentation.
