We introduce a learning strategy for contrast-invariant image registration without requiring imaging data. While classical registration methods accurately estimate the spatial correspondence between images, they solve a costly optimization problem for every image pair. Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model for diverse label maps and images that exposes networks to a wide range of variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary image contrasts without the need to retrain for new modalities. We demonstrate registration accuracy that most often surpasses the state of the art both within and across modalities, using a single model. Critically, we show that input labels from which we synthesize images need not be of actual anatomy: training on randomly generated geometric shapes also results in competitive registration performance, albeit slightly less accurate, while alleviating the dependency on real data of any kind. Our code is available at: http://voxelmorph.csail.mit.edu
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