Congratulations to Lin Tian, Hastings Greer who are presenting their CVPR paper on gradient inverse consistent image registraton (GradICON) this week. GradICON is a new deep-learning-based image registration approach which only weakly regularizes the tranformations via a new gradient inverse consistency loss.
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly …
Apparent changes in lung nodule size assessed via simple image-based measurements from computed tomography (CT) images may be compromised by the effect of the background lung tissue deformation on the nodule, leading to erroneous nodule tracking. We …
We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a deep registration framework which is trained using sets of digitally reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By using simulated training …
This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning methods for point cloud registration, boosting accuracy at an …
Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often …
In this paper a novel Large Deformation Diffeomorphic Metric Mapping (LDDMM) scheme is presented which has significantly lower computational and memory demands than standard LDDMM but achieves the same accuracy. We exploit the smoothness of …
We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal …
Traditional deformable image registration imposes a uniform smoothness constraint on the deformation field. This is not appropriate when registering images visualizing organs that slide relative to each other, and therefore leads to registration …
Regularization is used in deformable image registration to encourage plausible displacement fields, and significantly impacts the derived correspondences. Sliding motion, such as that between the lungs and chest wall and between the abdominal organs, …