Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Lung image registration can effectively describe the relative motion of lung tissues, thereby
helping to solve series problems in clinical applications. Since the lungs are soft and fairly …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Radiological images such as computed tomography (CT) and X-rays render anatomy with
intrinsic structures. Being able to reliably locate the same anatomical structure across …

This article presents a generic probabilistic framework for estimating the statistical
dependency and finding the anatomical correspondences among an arbitrary number of …

Over the past decade, deep learning technologies have greatly advanced the field of
medical image registration. The initial developments, such as ResNet-based and U-Net …

Learning-based registration for large-scale 3D point clouds has been shown to improve
robustness and accuracy compared to classical methods and can be trained without …

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

Unsupervised image registration commonly adopts U-Net style networks to predict dense
displacement fields in the full-resolution spatial domain. For high-resolution volumetric …

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