IEEE Trans Biomed Eng. 2020 Mar;67(3):924-930. doi: 10.1109/TBME.2019.2924398. Epub 2019 Jun 21.
Structural measurements after separation of cortical from trabecular bone are of interest to a wide variety of communities but are difficult to obtain because of the lack of accurate automated techniques.
We present a structure-based algorithm for separating cortical from trabecular bone in binarized images. Using the thickness of the cortex as a seed value, bone connected to the cortex within a spatially local threshold value is identified and separated from the remaining bone. The algorithm was tested on seven biological data sets from four species imaged using micro-computed tomography (μ-CT) and high-resolution peripheral quantitative computed tomography (HR-pQCT). Area and local thickness measurements were compared to images segmented manually.
The algorithm was approximately 11 times faster than manual measurements and the median error in cortical area was -4.47 ± 4.15%. The median error in cortical thickness was approximately 0.5 voxels for μ-CT data and less than 0.05 voxels for HR-pQCT images resulting in an overall difference of -28.1 ± 71.1 μm.
A simple and readily implementable methodology has been developed that is repeatable, efficient, and requires few user inputs, providing an unbiased means of separating cortical from trabecular bone.
Automating the segmentation of variably thick cortices will allow for the evaluation of large data sets in a time-efficient manner and allow for full-field analyses that have been previously limited to small regions of interest. The MATLAB code can be downloaded from https://github.com/TBL-UIUC/downloads.git.
皮质骨与松质骨分离后的结构测量对广泛的领域都具有重要意义，但由于缺乏准确的自动化技术，因此很难实现。
我们提出了一种基于结构的算法，用于将二进制图像中的皮质骨与松质骨分离。利用皮质骨的厚度作为种子值，确定与皮质骨在空间局部阈值内相连的骨并将其与其余骨分离。该算法在四个物种的七个生物数据集上进行了测试，这些数据集是使用微计算机断层扫描（μ-CT）和高分辨率外周定量计算机断层扫描（HR-pQCT）成像的。将面积和局部厚度测量值与手动分割的图像进行了比较。
该算法的速度比手动测量快约 11 倍，皮质骨面积的中位数误差为-4.47±4.15%。μ-CT 数据皮质厚度的中位数误差约为 0.5 个体素，HR-pQCT 图像的误差小于 0.05 个体素，总体差异为-28.1±71.1μm。
开发了一种简单、易于实现的方法，该方法具有可重复性、高效性且需要较少的用户输入，可以提供一种分离皮质骨与松质骨的无偏方法。
皮质骨厚度可变的自动分割将允许以高效的方式评估大型数据集，并允许进行以前仅限于小感兴趣区域的全场分析。MATLAB 代码可从 https://github.com/TBL-UIUC/downloads.git 下载。
