  Deformable image registration is a crucial step in medical image analysis for
finding a non-linear spatial transformation between a pair of fixed and moving
images. Deep registration methods based on Convolutional Neural Networks (CNNs)
have been widely used as they can perform image registration in a fast and
end-to-end manner. However, these methods usually have limited performance for
image pairs with large deformations. Recently, iterative deep registration
methods have been used to alleviate this limitation, where the transformations
are iteratively learned in a coarse-to-fine manner. However, iterative methods
inevitably prolong the registration runtime, and tend to learn separate image
features for each iteration, which hinders the features from being leveraged to
facilitate the registration at later iterations. In this study, we propose a
Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable
image registration. In the NICE-Net, we propose: (i) a Single-pass Deep
Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine
transformations within a single pass (iteration) of the network, and (ii) a
Selectively-propagated Feature Learning (SFL) encoder that can learn common
image features for the whole coarse-to-fine registration process and
selectively propagate the features as needed. Extensive experiments on six
public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our
proposed NICE-Net can outperform state-of-the-art iterative deep registration
methods while only requiring similar runtime to non-iterative methods.
