Predicting Shape Development: a Riemannian Method
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task. But it can be essential for clinical decision-making. Research has shown that it should be tackled in curved shape spaces, as (e.g., disease-related) shape changes frequently expose nonlinear characteristics. We thus propose a novel prediction method that encodes the whole shape in a Riemannian shape space. It then learns a simple prediction technique founded on hierarchical statistical modeling of longitudinal training data. When applied to predict the future development of the shape of the right hippocampus under Alzheimer's disease and to human body motion, it outperforms deep learning-supported variants as well as state-of-the-art.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingHippocampusSimilar Papers 제목 키워드 기반
Riemannian Geometry and Molecular Surfaces I: Spectrum of the Laplacian
Ligand-based virtual screening aims to reduce the cost and duration of drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing to mole…
Drug DiscoveryA Riemannian Framework for Analysis of Human Body Surface
We propose a novel framework for comparing 3D human shapes under the change of shape and pose. This problem is challenging since 3D human shapes vary significantly across subjects and body postures. We solve this problem…
RetrievalRiemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
We consider the problem of graph-matching on a network of 3D shapes with uncertainty quantification. We assume that the pairwise shape correspondences are efficiently represented as \emph{functional maps}, that match rea…
Graph MatchingUncertainty QuantificationBaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes
We present Basis Restricted Elastic Shape Analysis (BaRe-ESA), a novel Riemannian framework for human body scan representation, interpolation and extrapolation. BaRe-ESA operates directly on unregistered meshes, i.e., wi…
Finsler-Laplace-Beltrami Operators with Application to Shape Analysis
The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the Swiss army knife of geometry processing as it allows to capture intrinsic shape information an…