论文标题
从图像中学习人群级的形状统计和解剖学细分：联合深度学习模型
Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model
论文作者
论文摘要
统计形状建模是对解剖群体进行定量分析的重要工具。点分布模型（PDMS）通过一组密集的对应关系表示解剖表面，这是一种直观且易于使用的形状表示，用于后续应用。这些对应关系在两个坐标空间中显示：局部坐标描述了每个单独的解剖表面的几何特征，并且世界坐标代表了给定同胞中各个样本的全局一致性差异后，代表了种群级的统计形状信息。我们提出了一个基于学习的框架，该框架同时直接从体积图像中学习了这两个坐标空间。提议的联合模型具有双重目的；世界对应关系可直接用于形状分析应用程序，从而规避传统PDM模型中涉及的重型预处理和分割。另外，局部对应关系可用于解剖分割。我们证明了该联合模型对两个数据集上的形状建模应用的功效及其在推断解剖表面方面的效用。
Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibited in two coordinate spaces: the local coordinates describing the geometrical features of each individual anatomical surface and the world coordinates representing the population-level statistical shape information after removing global alignment differences across samples in the given cohort. We propose a deep-learning-based framework that simultaneously learns these two coordinate spaces directly from the volumetric images. The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models. Additionally, the local correspondences can be used for anatomy segmentation. We demonstrate the efficacy of this joint model for both shape modeling applications on two datasets and its utility in inferring the anatomical surface.
