This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …

3D medical image registration is of great clinical importance. However, supervised learning
methods require a large amount of accurately annotated corresponding control points (or …

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 …

In this paper, we introduce and compare different approaches for incorporating shape prior
information into neural network-based image segmentation. Specifically, we introduce the …

Background Deformable image registration is crucial for multiple radiation therapy
applications. Fast registration of computed tomography (CT) lung images is challenging …

We present a new unsupervised learning algorithm,“FAIM”, for 3D medical image
registration. With a different architecture than the popular “U-net”[10], the network takes a …

To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we
proposed a Multi-scale DIR framework with unsupervised Joint training of Convolutional …

In this paper, we propose HESIC, an end-to-end trainable deep network for stereo image
compression (SIC). To fully explore the mutual information across two stereo images, we use …