We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …

Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …

Image registration is a fundamental task in medical image analysis. Recently, many deep
learning based image registration methods have been extensively investigated due to their …

We present HyperMorph, a learning-based strategy for deformable image registration that
removes the need to tune important registration hyperparameters during training. Classical …

We develop a learning framework for building deformable templates, which play a
fundamental role in many image analysis and computational anatomy tasks. Conventional …

Classical diffeomorphic image registration methods, while being accurate, face the
challenges of high computational costs. Deep learning based approaches provide a fast …

This paper presents DeepFLASH, a novel network with efficient training and inference for
learning-based medical image registration. In contrast to existing approaches that learn …

Registration of brain MRI images requires to solve a deformation field, which is extremely
difficult in aligning intricate brain tissues, eg, subcortical nuclei, etc. Existing efforts resort to …

Image registration is an essential step in many medical image analysis tasks. Traditional
methods for image registration are primarily optimization-driven, finding the optimal …