Statistical shape models (SSMs) are an established way to geometrically
represent the anatomy of a population with various clinically relevant
applications. However, they typically require domain expertise and
labor-intensive manual segmentations or landmark annotations to generate.
Methods to estimate correspondences for SSMs typically learn with such labels
as supervision signals. We address these shortcomings by proposing an
unsupervised method that leverages deep geometric features and functional
correspondences to learn local and global shape structures across complex
anatomies simultaneously. Our pipeline significantly improves unsupervised
correspondence estimation for SSMs compared to baseline methods, even on highly
irregular surface topologies. We demonstrate this for two different anatomical
structures: the thyroid and a multi-chamber heart dataset. Furthermore, our
method is robust enough to learn from noisy neural network predictions,
enabling scaling SSMs to larger patient populations without manual annotation.

本研究提出一种无监督方法，利用深度几何特征和功能对应关系，同时学习复杂解剖学中的局部和全局形状结构，从而显著改善统计形状模型的无监督对应估计，并且精度高于基线方法，甚至在高度不规则的表面拓扑上也适用。