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 …

Recently, deep-learning-based approaches have been widely studied for deformable image
registration task. However, most efforts directly map the composite image representation to …

Significance: Analysis of modern large-scale, multicenter or diseased data requires
deformable registration algorithms that can cope with data of diverse nature. Objective: We …

Deformable image registration is a fundamental task in medical image analysis, aiming to
establish a dense and non-linear correspondence between a pair of images. Previous deep …

Medical image registration is one of the key processing steps for biomedical image analysis
such as cancer diagnosis. Recently, deep learning based supervised and unsupervised …

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

Feature selection is a critical step in deformable image registration. In particular, selecting
the most discriminative features that accurately and concisely describe complex …

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 …

Recent deep learning-based methods have shown promising results and runtime
advantages in deformable image registration. However, analyzing the effects of …

Deformable image registration is a fundamental problem in medical image analysis. During
the last years, several methods based on deep convolutional neural networks (CNN) proved …