Abstract: Significant breakthroughs in medical image registration have been achieved using deep neural networks (DNNs). However, DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training. To leverage the intensity information of abundant unlabeled images, unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However, finding a sufficiently robust measure can be challenging for specific registration applications. Weakly supervised registration methods use anatomical labels to estimate the deformation between images. High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images, whereas label images are extremely difficult to collect. In this paper, we propose a two-stage semi-supervised learning framework for medical image registration, which consists of unsupervised and weakly supervised registration networks. The proposed semi-supervised learning framework is trained with intensity information from available images, label information from a relatively small number of labeled images and pseudo-label information from unlabeled images. Experimental results on two datasets (cardiac and abdominal images) demonstrate the efficacy and efficiency of this method in intra- and inter-modality medical image registrations, as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available. Our code is publicly available at https://github.com/jdq818/SeRN.

Key words: medical image registration| semi-supervised learning| intra-modality| inter-modality