Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and require complete geometric proxies (e.g., high-resolution binary volumes or surface meshes) as input shapes to construct the SSM. Unordered 3D point cloud representations of shapes are more easily acquired from various medical imaging practices (e.g., thresholded images and surface scanning). Point cloud deep networks have recently achieved remarkable success in learning permutation-invariant features for different point cloud tasks (e.g., completion, semantic segmentation, classification). However, their application to learning SSM from point clouds is to-date unexplored. In this work, we demonstrate that existing point cloud encoder-decoder-based completion networks can provide an untapped potential for SSM, capturing population-level statistical representations of shapes while reducing the inference burden and relaxing the input requirement. We discuss the limitations of these techniques to the SSM application and suggest future improvements. Our work paves the way for further exploration of point cloud deep learning for SSM, a promising avenue for advancing shape analysis literature and broadening SSM to diverse use cases.

翻译：统计形状建模（SSM）是研究及量化解剖变异群体内解剖结构差异的重要工具。然而，传统的基于对应关系的SSM生成方法不仅推理过程复杂，且要求以完整几何代理（如高分辨率二值体素或表面网格）作为输入形状来构建SSM。相比之下，无序的三维点云形状表征更易从各类医学影像实践中获取（如阈值图像和表面扫描）。近年来，点云深度网络在完成、语义分割、分类等不同点云任务中，通过学习置换不变特征取得了显著成功。但其在基于点云学习SSM方面的应用至今尚属空白。本文研究表明，现有基于编码器-解码器的点云补全网络可为SSM提供尚未被发掘的潜力：既能捕捉种群水平的形状统计表征，又能降低推理负担并放宽输入要求。我们讨论了这些技术应用于SSM的局限性，并提出了未来改进方向。本工作为点云深度学习在SSM中的进一步探索铺平了道路，是推动形状分析文献发展、拓展SSM多样化应用场景的有前景的研究路径。