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

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

Deformable multi-modal medical image registration aligns the anatomical structures of
different modalities to the same coordinate system through a spatial transformation. Due to …

Objective: Deformable Image Registration (DIR) plays a significant role in quantifying
deformation in medical data. Recent Deep Learning methods have shown promising …

Deformable image registration plays a critical role in various clinical applications (eg, image
fusion, atlas creation, and tumors targeting). In radiation therapy, especially in the context of …

In recent years, deep learning (DL)-based registration technology has significantly improved
the calculation speed of medical image registration. Existing DL-based registration methods …

In recent decades, medical image registration technology has undergone significant
development, becoming one of the core technologies in medical image analysis. With the …

To facilitate both the detection and the interpretation of findings in chest X-rays, comparison
with a previous image of the same patient is very valuable to radiologists. Today, the most …

Image registration estimates spatial correspondences between a pair of images. These
estimates are typically obtained via numerical optimization or regression by a deep network …

Medical image registration aims to estimate the spatial transformation between two medical
images. It plays a crucial role in various medical applications, including longitudinal studies …