Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry
Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry
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DOI：
10.3389/fbioe.2019.00302
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发表时间：
2019-11-01
影响因子：
5.7
通讯作者：
Claes, P.
中科院分区：
文献类型：
--
作者：
Audenaert, E. A.;Pattyn, C.;Claes, P.
目的：统计形状建模为描述和分析人体解剖结构提供了一个强有力的工具。通过线性组合给定解剖实体的群体的形状的方差，统计形状模型（SSM）识别其主要的变化模式，并且可以将该群体的总方差近似为选定的阈值，同时降低其维度。尽管SSM已经使用了二十多年，但它们缺乏对其预测良好性的表征，特别是在定义这些模型是否实际上代表给定人群时。研究方法：本文介绍了迄今为止，据作者所知，考虑骨盆、股骨、髌骨、胫骨、腓骨、距骨和跟骨的最大程度的下肢解剖形状模型。本研究包括从271个下肢CT扫描获得的分段训练形状（n = 542）。不同的模型进行了评估的准确性，紧凑性，概括性以及特异性。结果如下：每个模型所需的训练样本的大小，以便它可以被认为是人口覆盖估计约200个样本，根据不同模型的泛化性能。同时，性别差异和模式的左右不对称的确定和特点。大小被认为是最明显的性别差异，而个体内的不对称性的变化是最明显的插入部位的肌肉。结论：对于旨在覆盖描述性研究的人群的模型，所需的训练样本数量应达到相当大的200个样本。性别歧视的几何形态测量方法得分很高，但是，它并没有在很大程度上优于传统的方法的基础上离散的措施。
Purpose: Statistical shape modeling provides a powerful tool for describing and analyzing human anatomy. By linearly combining the variance of the shape of a population of a given anatomical entity, statistical shape models (SSMs) identify its main modes of variation and may approximate the total variance of that population to a selected threshold, while reducing its dimensionality. Even though SSMs have been used for over two decades, they lack in characterization of their goodness of prediction, in particular when defining whether these models are actually representative for a given population. Methods: The current paper presents, to the authors' knowledge, the most extent lower limb anatomy shape model considering the pelvis, femur, patella, tibia, fibula, talus, and calcaneum to date. The present study includes the segmented training shapes (n = 542) obtained from 271 lower limb CT scans. The different models were evaluated in terms of accuracy, compactness, generalizability as well as specificity. Results: The size of training samples needed in each model so that it can be considered population covering was estimated to approximate around 200 samples, based on the generalizability properties of the different models. Simultaneously differences in gender and patterns in left-right asymmetry were identified and characterized. Size was found to be the most pronounced sexual discriminator whereas intra-individual variations in asymmetry were most pronounced at the insertion site of muscles. Conclusion: For models aimed at population covering descriptive studies, the number of training samples required should amount a sizeable 200 samples. The geometric morphometric method for sex discrimination scored excellent, however, it did not largely outperformed traditional methods based on discrete measures.