Abstract
We introduce Point2SSM, a novel unsupervised learning approach that can accurately construct correspondence-based statistical shape models (SSMs) of anatomy directly from point clouds. SSMs are crucial in clinical research for analyzing the population-level morphological variation in bones and organs. However, traditional methods for creating SSMs have limitations that hinder their widespread adoption, such as the need for noise-free surface meshes or binary volumes, reliance on assumptions or predefined templates, and simultaneous optimization of the entire cohort leading to lengthy inference times given new data. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. Deep learning on 3D point clouds has seen recent success in unsupervised representation learning, point-to-point matching, and shape correspondence; however, their application to constructing SSMs of anatomies is largely unexplored. In this work, we benchmark state-of-the-art point cloud deep networks on the task of SSM and demonstrate that they are not robust to the challenges of anatomical SSM, such as noisy, sparse, or incomplete input and significantly limited training data. Point2SSM addresses these challenges via an attention-based module that provides correspondence mappings from learned point features. We demonstrate that the proposed method significantly outperforms existing networks in terms of both accurate surface sampling and correspondence, better capturing population-level statistics.
Abstract (translated)
我们介绍了 Point2SSM，一种全新的无监督学习方法，可以从点云直接准确地构建解剖学的生物统计形状模型(SSMs)。SSMs在临床研究中对于分析骨骼和器官的级联形态变异非常重要。然而，传统的SSMs制作方法存在一些限制，这些限制妨碍了其广泛采用，例如需要无噪声的表面网格或二进制体积、依赖假设或预先定义的模板、以及同时优化整个群体，导致新数据下的推断时间变得非常长。Point2SSM通过提供一种数据驱动的解决方案，从 raw 点云推断出SSMs，从而减少了推断负担并增加了适用性，因为点云更容易获取。三维点云深度学习最近在无监督表示学习、点-点匹配和形状对应性方面取得了成功。然而，将其应用于构建解剖学的SSMs仍然未被充分探索。在这个工作中，我们基准了最先进的点云深度学习网络SSM任务的性能，并证明了它们对于解剖学SSM的挑战不具有较强的鲁棒性，例如噪声、稀疏或不完整输入，以及训练数据显著限制。Point2SSM通过提供一种注意力模块，从学习到的点特征提供形状对应映射，解决了这些挑战。我们证明了该方法在准确的表面采样和对应性方面显著优于现有的网络，更好地捕捉人口级统计。
URL
https://arxiv.org/abs/2305.14486
