Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied to reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective of providing interpretable results. The proposed framework defines a likelihood appearance probability and a prior label probability based on a generic shape function through a logistic function. A reference length parameter defined in the sigmoid controls the trade-off between shape and appearance information. The inference of shape parameters is performed within an Expectation-Maximisation approach in which a Gauss-Newton optimization stage provides an approximation of the posterior probability of the shape parameters. This framework is applied to the segmentation of cochlear structures from clinical CT images constrained by a 10-parameter shape model. It is evaluated on three different datasets, one of which includes more than 200 patient images. The results show performances comparable to supervised methods and better than previously proposed unsupervised ones. It also enables an analysis of parameter distributions and the quantification of segmentation uncertainty, including the effect of the shape model.

结合形状信息对于描绘医学图像中的许多器官和解剖结构至关重要。虽然以前的工作主要集中在应用于参考模板形状的参数空间变换，但在本文中，我们解决了用于分割医学图像的参数形状模型的贝叶斯推断，目的是提供可解释的结果。所提出的框架通过逻辑函数定义了基于通用形状函数的似然出现概率和先验标签概率。sigmoid 中定义的参考长度参数控制形状和外观信息之间的权衡。形状参数的推断是在期望最大化方法中执行的，其中高斯-牛顿优化阶段提供形状参数的后验概率的近似值。该框架适用于从受 10 参数形状模型约束的临床 CT 图像中分割耳蜗结构。它在三个不同的数据集上进行评估，其中一个包含 200 多张患者图像。结果显示性能可与监督方法相媲美，并且优于先前提出的无监督方法。它还可以分析参数分布和量化分割不确定性，包括形状模型的影响。该框架适用于从受 10 参数形状模型约束的临床 CT 图像中分割耳蜗结构。它在三个不同的数据集上进行评估，其中一个包含 200 多张患者图像。结果显示性能可与监督方法相媲美，并且优于先前提出的无监督方法。它还可以分析参数分布和量化分割不确定性，包括形状模型的影响。该框架适用于从受 10 参数形状模型约束的临床 CT 图像中分割耳蜗结构。它在三个不同的数据集上进行评估，其中一个包含 200 多张患者图像。结果显示性能可与监督方法相媲美，并且优于先前提出的无监督方法。它还可以分析参数分布和量化分割不确定性，包括形状模型的影响。