We discuss recent advances in diffeomorphic image registration and related correspondence and shape matching problems. Diffeomorphic image registration is a classical, ill-posed, non-linear, non-convex, inverse problem with numerous applications in imaging sciences. It typically involves an infinite number of unknowns, which, upon discretization, results in high-dimensional, ill-conditioned systems. Image registration poses significant numerical challenges. We will showcase state-of-the-art techniques in scientific computing to tackle these challenges, highlight new theoretical developments, and discuss challenging application scenarios that require tailored formulations to obtain plausible solutions.
- Modelling and complexity issues on large deformations for shape ensembles
- Alain Trouvé (Centre de Mathématiques et Leurs Applications)
- Optimal transport for diffeomorphic registration
- François-Xavier Vialard (University Paris-Dauphine)
- A Lagrangian Framework for Fast and Flexible Diffeomorphic Image Registration
- Lars Ruthotto (Department of Mathematics and Computer Science, Emory University)
- Statistically-constrained Robust Diffeomorphic Registration
- Aristeidis Sotiras (University of Pennsylvania)
- Non-parametric registration of medical image data using Schatten-q-Norms
- Kai Brehmer (Institute of Mathematics and Image Computing, University of Lübeck)
- Machine Learning Approaches for Deformable Image Registration
- Marc Niethammer (University of North Carolina at Chapel Hill)
- GPU Based Geodesics of Image Time Series
- Benjamin Berkels (RWTH Aachen University)
- CLAIRE: A parallel solver for constrained large deformation diffeomorphic image registration
- Andreas Mang (Department of Mathematics, University of Houston)
- Organizers:
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George Biros (Institute for Computational Engineering and Sciences, University of Texas at Austin)
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Andreas Mang (Department of Mathematics, University of Houston)
- Keywords:
- computer vision, image registration, inverse problems, nonlinear optimization