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Overall Bone Structure as Assessed by Slice-by-Slice Profile

机译:通过逐层剖面评估的整体骨骼结构

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Quantifying the inner structure of bones is central to various analyses dealing with the phenotypic evolution of animals with an ossified skeleton. Computed tomography allows to assess the repartition of bone tissue within an entire skeletal element. Two parameters of importance for such analyses are the global compactness (Cg) and total cross-sectional area (Tt.Ar). However, no open-source, time-efficient methods are available to acquire these parameters for whole bones. A methodology to assess the variation of these parameters along a profile following one of the studied bone's anatomical axes is also wanting. Here I present an ImageJ macro and associated R script to automatically acquire Cg and Tt.Ar along an axis of the skeletal element of interest using a slice-by-slice approach. No manual segmentation is required and several bones can be present on the analysed scan, as long as the bone of interest is isolated and the largest element on each slice. While some bias might be involved by the automatic acquisition, semi-automatic slice exclusion and correction procedures can be used to efficiently account for it. As a test case, mu CT-data was gathered for the mid-lumbar vertebra of over 70 mammals. The two evaluated correction procedures proved to perform equally well, with a slight advantage for the one relying on the exclusion of local outliers. The presented macro allows to efficiently build a dataset concerned with the quantification of bone inner structure. The code being readily available, further improvement of the methodology and adjustment to particular needs can be easily performed.
机译:量化骨骼的内部结构是处理骨骼化动物表型进化的各种分析的核心。计算机断层扫描可以评估整个骨骼元素内骨组织的重新分配。对于此类分析来说,两个重要的参数是全局密实度 (Cg) 和总横截面积 (Tt.Ar)。然而,没有开源的、省时的方法来获取整个骨骼的这些参数。还需要一种方法来评估这些参数沿着所研究骨骼解剖轴之一的轮廓变化。在这里,我展示了一个 ImageJ 宏和关联的 R 脚本,用于使用逐个切片方法沿感兴趣的骨架元素的轴自动获取 Cg 和 Tt.Ar。不需要手动分割,只要分离出感兴趣的骨骼并且每个切片上最大的元素,分析扫描中就可以存在几块骨头。虽然自动采集可能涉及一些偏差,但可以使用半自动切片排除和校正程序来有效地解释它。作为测试案例,收集了 70 多种哺乳动物腰椎中段的 mu CT 数据。事实证明,两种评估的校正程序表现同样出色,而依赖于排除局部异常值的校正程序略有优势。所提出的宏可以有效地构建一个与骨骼内部结构量化的数据集。代码随时可用,可以很容易地进一步改进方法并根据特定需求进行调整。

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