Fan Jingfan, Cao Xiaohuan, Xue Zhong, Yap Pew-Thian, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Med Image Comput Comput Assist Interv. 2018 Sep;11070:739-746. doi: 10.1007/978-3-030-00928-1_83. Epub 2018 Sep 26.
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network. Experiments on four brain MRI datasets indicate that our method yields registration performance that is promising in both accuracy and efficiency compared with state-of-the-art registration methods, including those based on deep learning.
本文介绍了一种用于图像配准的无监督对抗相似性网络。与现有的深度学习配准框架不同，我们的方法不需要真实变形和特定的相似性度量。我们通过一个可变形变换层连接配准网络和判别网络。配准网络利用判别网络的反馈进行训练，判别网络旨在判断一对配准图像是否足够相似。通过对抗训练，配准网络被训练来预测足够准确的变形，以欺骗判别网络。在四个脑磁共振成像数据集上的实验表明，与包括基于深度学习的方法在内的现有最先进配准方法相比，我们的方法在准确性和效率方面都具有良好的配准性能。
