Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint

TL;DR

This paper introduces a dual-flow transformation network with a region consistency constraint that improves deformable image registration by focusing on ROI similarity and estimating both global and regional transformations, leading to superior accuracy.

Contribution

The paper proposes a novel dual-flow network that incorporates region-level constraints to enhance registration accuracy and regional alignment in medical images.

Findings

Achieves state-of-the-art registration accuracy on 3D MRI datasets.

Effectively models both global and regional transformations.

Improves ROI alignment compared to existing methods.

Abstract

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.