We present a novel algorithm for the registration of pulmonary CT scans. Our method is
designed for large respiratory motion by integrating sparse keypoint correspondences into a …

We present an efficient neural network method for locating anatomical landmarks in 3D
medical CT scans, using atlas location autocontext in order to learn long-range spatial …

Purpose To automatically and precisely detect a large quantity of landmark pairs between
two lung computed tomography (CT) images to support evaluation of deformable image …

Computed tomography (CT)-derived ventilation imaging utilizes deformable image
registration (DIR) to recover respiratory-induced tissue volume changes from inhale/exhale …

Background Disease or injury may cause a change in the biomechanical properties of the
lungs, which can alter lung function. Image registration can be used to measure lung …

In this thesis three diffeomorphic image registration methods are proposed. A Discretize-
then-Optimize approach is used to derive these methods from optimal control formulations in …

We introduce a new highly parallel and memory efficient deformable image registration
algorithm to handle challenging clinical applications. The algorithm is based on the …

Large deformation diffeomorphic metric mapping (LDDMM) is a popular approach for
deformable image registration with nice mathematical properties. LDDMM encodes spatial …

Non-rigid image registration is prone to non-realistic deformations. In this paper, we
proposed a novel landmark-correspondence detection algorithm, with which, the non …

Object-based image analysis (OBIA) is a concept for analyzing images based on regions
instead of pixels. OBIA allows to effectively incorporate features of regions as well as their …