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 …

The alignment of images through deformable image registration is vital to clinical
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …

The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …

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

Registration for multisensor or multimodal image pairs with a large degree of distortions is a
fundamental task for many remote sensing applications. To achieve accurate and low-cost …

Image registration, the process of aligning two or more images, is the core technique of
many (semi-) automatic medical image analysis tasks. Recent studies have shown that deep …

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

Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …

Diffeomorphic deformable image registration is crucial in many medical image studies, as it
offers unique, special features including topology preservation and invertibility of the …

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 …