TY - CONF A1 - Türkseven, Doğa A1 - Rekik, Islem A1 - von Tycowicz, Christoph A1 - Hanik, Martin T1 - Predicting Shape Development: A Riemannian Method T2 - Shape in Medical Imaging N2 - 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. Y1 - 2023 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9183 SP - 211 EP - 222 PB - Springer Nature ER -