  Data-driven deep learning approaches to image registration can be less
accurate than conventional iterative approaches, especially when training data
is limited. To address this whilst retaining the fast inference speed of deep
learning, we propose VR-Net, a novel cascaded variational network for
unsupervised deformable image registration. Using the variable splitting
optimization scheme, we first convert the image registration problem,
established in a generic variational framework, into two sub-problems, one with
a point-wise, closed-form solution while the other one is a denoising problem.
We then propose two neural layers (i.e. warping layer and intensity consistency
layer) to model the analytical solution and a residual U-Net to formulate the
denoising problem (i.e. generalized denoising layer). Finally, we cascade the
warping layer, intensity consistency layer, and generalized denoising layer to
form the VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac
magnetic resonance imaging datasets show that VR-Net outperforms
state-of-the-art deep learning methods on registration accuracy, while
maintains the fast inference speed of deep learning and the data-efficiency of
variational model.
