Predicting the future development of an anatomical shape from a single baseline is an important but difficult problem to solve. 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 that is founded on statistical hierarchical modelling of longitudinal training data. It is fully automatic, which makes it stand out in contrast to parameter-rich state-of-the-art methods. When applied to predict the future development of the shape of right hippocampi under Alzheimer's disease, it outperforms deep learning supported variants and achieves results on par with state-of-the-art.

PtyChography是一种经过良好研究的相成像方法，可在纳米尺度上进行非侵入性成像。它已发展为主流技术，在材料科学或国防工业等各个领域具有各种应用。 PtyChography的一个主要缺点是由于相邻照明区域之间的高重叠要求以实现合理的重建，因此数据采集时间很长。扫描区域之间重叠的传统方法导致与文物的重建。在本文中，我们提出了从深层生成网络采样的数据中稀疏获得或不足采样的数据，以满足Ptychography的过采样要求。由于深度生成网络是预先训练的，并且可以在收集数据时计算其输出，因此可以减少实验数据和获取数据的时间。我们通过提出重建质量与先前提出的和传统方法相比，通过提出重建质量来验证该方法，并评论提出的方法的优势和缺点。