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Multi-modality image registration is one of the most underlined processes inmedical image analysis. Recently, convolutional neural networks (CNNs) haveshown significant potential in deformable registration. However, the lack ofvoxel-wise ground truth challenges the training of CNNs for an accurateregistration. In this work, we propose a cross-modality similarity metric,based on the KL-divergence of image variables, and implement an efficientestimation method using a CNN. This estimation network, referred to asKLDivNet, can be trained unsupervisedly. We then embed the KLDivNet into aregistration network to achieve the unsupervised deformable registration formulti-modality images. We employed three datasets, i.e., AAL Brain, LiTS Liverand Hospital Liver, with both the intra- and inter-modality image registrationtasks for validation. Results showed that our similarity metric was effective,and the proposed registration network delivered superior performance comparedto the state-of-the-art methods.

KLDivNet: An unsupervised neural network for multi-modality image registration