Generative Adversarial Network with Patch Selection for Deformable Registration of Medical Images

Image registration is a fundamental topic in medical image research. Traditional registration methods have a large computational cost and longtime consumption in the optimization process. Recently, learning-based registration frameworks have not only improved computational speed but also greatly enhanced registration performance. However, local registration accuracy is often difficult to maintain during registration for images with complex deformation. Therefore, we proposed a generative adversarial network with an image patch selection strategy and attention block. The discriminator applies additional regularization to image patches with complex deformations, while improving overall registration accuracy. We also introduced the anti-folding and smoothness loss in the registration network to generate reasonable deformation fields. The experimental results demonstrate that compared with some current studies, the proposed method achieves better registration performance, especially in regions with complex deformations.