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Automatic multi-resolution shape modeling of multi-organ structures

机译：多器官结构的自动多分辨率形状建模

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Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI. (C) 2015 Elsevier B.V. All rights reserved.
机译：点分布模型（PDM）是最流行的形状描述技术之一，其用途已在各种医学成像应用中得到证明。但是，要充分刻画基础建模人口的特征，必不可少的是要有代表性的训练样本数量。随着建模结构的复杂性的增加，这个问题变得尤为重要，因为这是最具挑战性的情况之一，即对多个3D器官的集合进行建模。在本文中，我们在多器官分析的背景下介绍了一种新的通用通用多分辨率PDM（GEM-PDM），它能够有效地表征不同的对象间关系以及每个对象的特定位置。重要的是，与以前的方法不同，由于此处提出了一种新的聚集地标聚类方法，该算法的配置是自动化的，它同样使我们能够识别器官内较小的解剖学重要区域。就形状建模的准确性和对噪声的鲁棒性而言，GEM-PDM方法相对于先前的两种方法（PDM和分层PDM）的显着优势已经成功地针对两个不同的多器官集合数据库进行了验证：六个皮层下大脑结构，以及七个腹部器官。最后，我们建议将新的形状建模框架集成到基于活动形状模型的分割算法中。当应用于3D脑MRI的分割时，由此产生的名为GEMA的算法与经过测试的两种经典方法ASM和分层ASM相比，提供了更好的整体性能。 （C）2015 Elsevier B.V.保留所有权利。

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