  Deformable image registration is able to achieve fast and accurate alignment
between a pair of images and thus plays an important role in many medical image
studies. The current deep learning (DL)-based image registration approaches
directly learn the spatial transformation from one image to another by
leveraging a convolutional neural network, requiring ground truth or similarity
metric. Nevertheless, these methods only use a global similarity energy
function to evaluate the similarity of a pair of images, which ignores the
similarity of regions of interest (ROIs) within images. Moreover, DL-based
methods often estimate global spatial transformations of image directly, which
never pays attention to region spatial transformations of ROIs within images.
In this paper, we present a novel dual-flow transformation network with region
consistency constraint which maximizes the similarity of ROIs within a pair of
images and estimates both global and region spatial transformations
simultaneously. Experiments on four public 3D MRI datasets show that the
proposed method achieves the best registration performance in accuracy and
generalization compared with other state-of-the-art methods.
