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We present VoxelMorph, a fast learning-based framework for deformable,pairwise medical image registration. Traditional registration methods optimizean objective function for each pair of images, which can be time-consuming forlarge datasets or rich deformation models. In contrast to this approach, andbuilding on recent learning-based methods, we formulate registration as afunction that maps an input image pair to a deformation field that aligns theseimages. We parameterize the function via a convolutional neural network (CNN),and optimize the parameters of the neural network on a set of images. Given anew pair of scans, VoxelMorph rapidly computes a deformation field by directlyevaluating the function. In this work, we explore two different trainingstrategies. In the first (unsupervised) setting, we train the model to maximizestandard image matching objective functions that are based on the imageintensities. In the second setting, we leverage auxiliary segmentationsavailable in the training data. We demonstrate that the unsupervised model'saccuracy is comparable to state-of-the-art methods, while operating orders ofmagnitude faster. We also show that VoxelMorph trained with auxiliary dataimproves registration accuracy at test time, and evaluate the effect oftraining set size on registration. Our method promises to speed up medicalimage analysis and processing pipelines, while facilitating novel directions inlearning-based registration and its applications. Our code is freely availableat voxelmorph.csail.mit.edu.

VoxelMorph: A Learning Framework for Deformable Medical Image Registration