Deformable Image Registration Using Attentional Generative Adversarial Networks

Abstract

Deformable image registration is a fundamental process that aims to estimate non-linear spatial correspondence between input images. Medical image registration serves wildly on clinical treatment evaluation, monitoring disease and tracking disease. Conventional registration algorithms iteratively optimize a similarity function for each pair of images, which result in long registration time. Learning based registration method typically use convolutional neural networks to learn features automatically during training and register an image pair in one shot. Generative adversarial network is a novel structure that involves a generator and a discriminator, where the discriminator encourages the former to generate better results. Predicting deformation between brain magnetic resonance images is a complicated task because of their high-dimensional non-linear transform. A generative model leveraging is proposed using an attentional mechanism to estimate the complicated deformation field, and train the model using perceptual cyclic constraints. As an unsupervised method, our model dose not need any labels for training. Experimental results show quantitative evidence that the proposed method can predict reliable deformation field at a fast speed.