Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as
skull-strip**, is an integral component of many neuroimage analysis streams. Despite their …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

Volumetric registration of brain MRI is routinely used in human neuroimaging, eg, to align
different MRI modalities, to measure change in longitudinal analysis, to map an individual to …

Classical optimization and learning-based methods are the two reigning paradigms in
deformable image registration. While optimization-based methods boast generalizability …

We present KeyMorph, a deep learning-based image registration framework that relies on
automatically detecting corresponding keypoints. State-of-the-art deep learning methods for …

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

We present an approach to learning regular spatial transformations between image pairs in
the context of medical image registration. Contrary to optimization-based registration …

Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …