Cerrolaza Juan J, Villanueva Arantxa, Reyes Mauricio, Cabeza Rafael, González Ballester Miguel Angel, Linguraru Marius George
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):1-8. doi: 10.1007/978-3-319-10443-0_1.
Point Distribution Models (PDM) are some of the most popular shape description techniques in medical imaging. However, to create an accurate shape model it is essential to have a representative sample of the underlying population, which is often challenging. This problem is particularly relevant as the dimensionality of the modeled structures increases, and becomes critical when dealing with complex 3D shapes. In this paper, we introduce a new generalized multiresolution hierarchical PDM (GMRH-PDM) able to efficiently address the high-dimension-low-sample-size challenge when modeling complex structures. Unlike previous approaches, our new and general framework extends hierarchical modeling to any type of structure (multi- and single-object shapes) allowing to describe efficiently the shape variability at different levels of resolution. Importantly, the configuration of the algorithm is automatized thanks to the new agglomerative landmark clustering method presented here. Our new and automatic GMRH-PDM framework performed significantly better than classical approaches, and as well as the state-of-the-art with the best manual configuration. Evaluations have been studied for two different cases, the right kidney, and a multi-object case composed of eight subcortical structures.
点分布模型（PDM）是医学成像中最流行的一些形状描述技术。然而，要创建一个准确的形状模型，拥有基础人群的代表性样本至关重要，而这通常具有挑战性。随着所建模结构的维度增加，这个问题尤其突出，在处理复杂的三维形状时变得至关重要。在本文中，我们引入了一种新的广义多分辨率分层PDM（GMRH-PDM），它能够在对复杂结构进行建模时有效应对高维度低样本量的挑战。与以前的方法不同，我们新的通用框架将分层建模扩展到任何类型的结构（多对象和单对象形状），从而能够在不同分辨率级别上有效地描述形状变化。重要的是，由于本文提出的新的凝聚地标聚类方法，算法的配置实现了自动化。我们新的自动GMRH-PDM框架的性能明显优于传统方法，与最佳手动配置的现有技术水平相当。针对两种不同情况进行了评估，即右肾以及由八个皮质下结构组成的多对象情况。
