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 require a time-consuming re-optimization process each time a new subject is added to the cohort, making the inference process prohibitive for clinical research. Additionally, they 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）是研究和量化解剖群体内解剖变异的宝贵工具。然而，传统的基于对应关系的SSM生成方法每次向队列添加新样本时都需要耗时的重新优化过程，这使得推理过程难以应用于临床研究。此外，这些方法需要完整的几何代理（例如高分辨率二值体或表面网格）作为输入形状来构建SSM。从各种医学成像实践（例如阈值化图像和表面扫描）中更容易获取无序的三维点云表示。点云深度网络最近在学习不同点云任务（如补全、语义分割、分类）的置换不变特征方面取得了显著成功。然而，目前尚未探索将其应用于从点云中学习SSM。在本工作中，我们证明现有的基于编码器-解码器的点云补全网络可以为SSM提供未开发的潜力，在捕获群体级别的形状统计表示的同时，减轻推理负担并放宽输入要求。我们讨论了这些技术在SSM应用中的局限性，并提出了未来改进方向。我们的工作为进一步探索用于SSM的点云深度学习铺平了道路，这是推进形状分析文献并将SSM扩展到多样化应用场景的一个有前景的途径。