•A deep learning approach for image registration to predict the deformation field in one-pass and is insensitive to parameter tuning.•Hierarchical dual-supervised fully convolutional neural network (FCN) to deal with the lack of ground truth for training.•The deep convolutional network is further improved with gap filling, hierarchical loss, and multi-source strategies. In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods. Display omitted