Multi-strategy mutual learning network for deformable medical image registration

Abstract

Deformable medical image registration plays a vital role in clinical diagnosis, monitoring treatment, and postoperative recovery. Nevertheless, the existing registration algorithms rely on a single network or training strategy to complete the registration task. Even the most advanced registration algorithms cannot capture sufficiently compelling feature information by a single network or strategy.This paper proposes a new unsupervised and deformable medical image registration network framework to get the compelling feature. The network uses 2D sub-images of 3D images as additional constraint information to supplement the training process. Therefore, the network’s two-dimensional and three-dimensional data images can interactively learn each other’s characteristic information. In addition, we propose to perform secondary registration on the concentrated part of the registration area according to the characteristics of the input image. It enables the complete image and critical regions to learn their characteristic information interactively. This paper uses the image pyramid to integrate the two mutual learning strategies, thus proposing a multi-strategy mutual learning network(MMLN) and conducting many evaluation tests on the public data set OASIS and LPBA40. The test results show that the network can achieve better registration performance than other learning-based methods and traditional algorithms.