Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint
This work addresses a domain-specific problem in medical image analysis by improving registration accuracy for MRI data, representing an incremental advancement over existing deep learning methods.
The paper tackles the problem of deformable image registration in medical imaging by introducing a dual-flow transformation network with region consistency constraint, which simultaneously maximizes similarity of regions of interest and estimates global and region spatial transformations, achieving the best registration performance in accuracy and generalization on four public 3D MRI datasets.
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.