For image registration, feature detection and description are critical steps that identify the
keypoints and describe them for the subsequent matching to estimate the geometric …

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 …

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver
registration for image-pairs works by patch-wise prediction of a deformation model based …

In this paper, we propose a deep learning approach for image registration by predicting
deformation from image appearance. Since obtaining ground-truth deformation fields for …

We present a method to predict image deformations based on patch-wise image
appearance. Specifically, we design a patch-based deep encoder-decoder network which …

This paper presents DeepFLASH, a novel network with efficient training and inference for
learning-based medical image registration. In contrast to existing approaches that learn …

Image registration is a key technique in medical image analysis to estimate deformations
between image pairs. A good deformation model is important for high-quality estimates …

This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation
spaces generated by a well known Euler-Poincaré differential equation (EPDiff). To achieve …

We introduce a deep encoder-decoder architecture for image deformation prediction from
multimodal images. Specifically, we design an image-patch-based deep network that jointly …

Image registration is essential for medical image analysis to provide spatial
correspondences. It is a difficult problem due to the modeling complexity of image …