The use of animal models along with the employment of advanced and sophisticated stereological methods for assessing bone quality combined with the use of statistical methods to evaluate the effectiveness of bone therapies has made it possible to investigate the pathways that regulate bone responses to medical devices. Image analysis of histomorphometric measurements remains a time-consuming task, as the image analysis software currently available does not allow for automated image segmentation. Such a feature is usually obtained by machine learning and with software platforms that provide image-processing tools such as MATLAB. In this study, we introduce a new MATLAB algorithm to quantify immunohistochemically stained critical-sized bone defect samples and compare the results with the commonly available Aperio Image Scope Positive Pixel Count (PPC) algorithm. Bland and Altman analysis and Pearson correlation showed that the measurements acquired with the new MATLAB algorithm were in excellent agreement with the measurements obtained with the Aperio PPC algorithm, and no significant differences were found within the histomorphometric measurements. The ability to segment whole slide images, as well as defining the size and the number of regions of interest to be quantified, makes this MATLAB algorithm a potential histomorphometric tool for obtaining more objective, precise, and reproducible quantitative assessments of entire critical-sized bone defect image data sets in an efficient and manageable workflow.

中文翻译：

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一种新的自动组织形态计量学 MATLAB 算法，用于使用全玻片成像进行免疫组织化学分析。

动物模型的使用以及用于评估骨骼质量的先进和复杂的立体学方法的使用，结合使用统计方法来评估骨骼疗法的有效性，使得研究调节骨骼对医疗器械反应的途径成为可能。组织形态测量的图像分析仍然是一项耗时的任务，因为当前可用的图像分析软件不允许自动图像分割。这种特征通常是通过机器学习和提供图像处理工具（如 MATLAB）的软件平台获得的。在这项研究中，我们引入了一种新的 MATLAB 算法来量化免疫组织化学染色的临界尺寸骨缺损样本，并将结果与​​常用的 Aperio Image Scope Positive Pixel Count (PPC) 算法进行比较。Bland 和 Altman 分析以及 Pearson 相关性表明，使用新 MATLAB 算法获得的测量结果与使用 Aperio PPC 算法获得的测量结果非常一致，并且在组织形态测量测量中没有发现显着差异。分割整个幻灯片图像的能力，以及定义要量化的感兴趣区域的大小和数量，使这种 MATLAB 算法成为潜在的组织形态测量工具，用于获得更客观、更精确、