Boyer Doug M, Puente Jesus, Gladman Justin T, Glynn Chris, Mukherjee Sayan, Yapuncich Gabriel S, Daubechies Ingrid
Department of Evolutionary Anthropology, Duke University, Durham, North Carolina.
Anat Rec (Hoboken). 2015 Jan;298(1):249-76. doi: 10.1002/ar.23084.
Three-dimensional geometric morphometric (3DGM) methods for placing landmarks on digitized bones have become increasingly sophisticated in the last 20 years, including greater degrees of automation. One aspect shared by all 3DGM methods is that the researcher must designate initial landmarks. Thus, researcher interpretations of homology and correspondence are required for and influence representations of shape. We present an algorithm allowing fully automatic placement of correspondence points on samples of 3D digital models representing bones of different individuals/species, which can then be input into standard 3DGM software and analyzed with dimension reduction techniques. We test this algorithm against several samples, primarily a dataset of 106 primate calcanei represented by 1,024 correspondence points per bone. Results of our automated analysis of these samples are compared to a published study using a traditional 3DGM approach with 27 landmarks on each bone. Data were analyzed with morphologika(2.5) and PAST. Our analyses returned strong correlations between principal component scores, similar variance partitioning among components, and similarities between the shape spaces generated by the automatic and traditional methods. While cluster analyses of both automatically generated and traditional datasets produced broadly similar patterns, there were also differences. Overall these results suggest to us that automatic quantifications can lead to shape spaces that are as meaningful as those based on observer landmarks, thereby presenting potential to save time in data collection, increase completeness of morphological quantification, eliminate observer error, and allow comparisons of shape diversity between different types of bones. We provide an R package for implementing this analysis.
在过去20年里，用于在数字化骨骼上放置地标点的三维几何形态测量（3DGM）方法变得越来越复杂，包括更高程度的自动化。所有3DGM方法的一个共同特点是，研究人员必须指定初始地标点。因此，研究人员对同源性和对应性的解释对于形状表示是必需的，并且会影响形状表示。我们提出了一种算法，可在代表不同个体/物种骨骼的三维数字模型样本上全自动放置对应点，然后可将这些点输入到标准3DGM软件中，并使用降维技术进行分析。我们针对几个样本测试了该算法，主要是一个包含106个灵长类跟骨的数据集，每块骨头由1024个对应点表示。我们对这些样本的自动分析结果与一项已发表的研究进行了比较，该研究使用传统3DGM方法，每块骨头有27个地标点。使用morphologika(2.5)和PAST对数据进行了分析。我们的分析得出主成分得分之间有很强的相关性，各成分之间的方差划分相似，以及自动方法和传统方法生成的形状空间之间有相似性。虽然对自动生成的数据集和传统数据集的聚类分析产生了大致相似的模式，但也存在差异。总体而言，这些结果向我们表明，自动量化可以产生与基于观察者地标点的形状空间同样有意义的形状空间，从而在数据收集方面有节省时间的潜力，提高形态量化的完整性，消除观察者误差，并允许比较不同类型骨头之间的形状多样性。我们提供了一个用于实现此分析的R包。
