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 on reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective to provide 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 where a Gauss-Newton optimization stage allows to provide an approximation of the posterior probability of shape parameters. This framework is applied to the segmentation of cochlea 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.
翻译:整合形状信息对于医学图像中许多器官和解剖结构的划界至关重要。 虽然先前的工作主要侧重于参考模板形状上应用的参数空间变换, 但在本文件中, 我们处理用于分割医学图像的巴伊西亚参数形状模型的推论, 目的是提供可解释的结果 。 拟议的框架定义了可能性外观概率和先前的标签概率, 其依据是一个通用形状函数, 是通过后勤功能。 示意图中定义的参考长度参数控制着形状和外观信息之间的取舍。 形状参数的推论是在期待- 最大化方法中进行的, 高斯- 纽顿优化阶段允许提供形状参数参数的外观概率近似值。 这个框架用于临床CT图像中受10个参数形状模型制约的 Cochlea结构的分解。 它根据三个不同的数据集进行了评估, 其中之一是200多张病人图像。 其结果显示的性能与所监督的方法相近, 并且比先前提议的未受监督的图像要好。 它还能够分析参数分布和分解形状的确定性效果, 。