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 …

The Computational Anatomy project is the morphome-scale study of shape and form, which
we model as an orbit under diffeomorphic group action. Metric comparison calculates the …

We propose a generic method for the statistical analysis of collections of anatomical shape
complexes, namely sets of surfaces that were previously segmented and labeled in a group …

Recent methodological innovations in deep learning and associated advancements in
computational hardware have significantly impacted the various core subfields of …

We introduce an end-to-end deep-learning framework for 3D medical image registration. In
contrast to existing approaches, our framework combines two registration methods: an affine …

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 …

In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a
3-dimensional image, learns to deform a reference template towards a targeted object. To …