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

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 …

DTI is one of the most effective MR tools for the investigation of the brain anatomy. In
addition to the gray matter, histopathological studies indicate that white matter is also a good …

The development of the brain is structure-specific, and the growth rate of each structure
differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used …

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

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively
analyze the status of the white matter anatomy in a tract-specific manner in many types of …

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain.
To optimize the usefulness for neonatal and pediatric care, systematic research, based on …

Registration of diffusion-weighted images is an important step in comparing white matter
fibre bundles across subjects, or in the same subject at different time points. Using diffusion …

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