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Brain image registration transforms a pair of images into one system with thematched imaging contents, which is of essential importance for brain imageanalysis. This paper presents a novel framework for unsupervised 3D brain imageregistration by capturing the feature-level transformation relationshipsbetween the unaligned image and reference image. To achieve this, we develop afeature-level probabilistic model to provide the direct regularization to thehidden layers of two deep convolutional neural networks, which are constructedfrom two input images. This model design is developed into multiple layers ofthese two networks to capture the transformation relationships at differentlevels. We employ two common benchmark datasets for 3D brain image registrationand perform various experiments to evaluate our method. Experimental resultsshow that our method clearly outperforms state-of-the-art methods on bothbenchmark datasets by a large margin.

Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration