Robles-Bykbaev Yaroslava, Naya Salvador, Díaz-Prado Silvia, Calle-López Daniel, Robles-Bykbaev Vladimir, Garzón Luis, Sanjurjo-Rodríguez Clara, Tarrío-Saavedra Javier
Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Departamento de Medicina, Universidade da Coruña, A Coruña, Spain.
Cátedra UNESCO UPS Tecnologías de apoyo para la Inclusión Educativa, Universidad Politécnica Salesiana, Cuenca, Ecuador.
PeerJ. 2019 Jul 5;7:e7233. doi: 10.7717/peerj.7233. eCollection 2019.
This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects' circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.
这项工作提出了一种基于图像分析以及机器学习和统计学习的方法，用于对骨细胞生长（在用于骨再生系统的I型胶原蛋白支架中）以及由于细胞生长导致的胶原蛋白降解程度进行建模和估计。为实现这些目标，在模拟人体组织的条件下，在2个月中的每个月的3天内测量了受骨细胞生长和干细胞分化作用的胶原蛋白质量。此外，应用光学显微镜来获取有关细胞生长、细胞分化和胶原蛋白降解的信息。我们的第一项贡献在于将监督分类随机森林算法应用于图像纹理特征（结构张量和熵），以估计通过光学显微镜获得的图像中的不同感兴趣区域：细胞外基质、胶原蛋白、图像背景和细胞核。然后，通过提取与细胞生长和胶原蛋白降解进程相关的特征（例如物体的平均面积和强度直方图的众数）来确定细胞外基质和胶原蛋白的感兴趣区域。最后，这些关键特征通过非参数和参数线性及非线性模型（如基于逻辑函数的模型）根据时间进行统计建模。具体而言，参数逻辑混合模型提供了一种通过估计相应的质量损失比例来识别和建模由于生物活性导致的降解的方法。还确定了一方面骨细胞从干细胞的生长和分化与另一方面胶原蛋白降解之间的关系，并通过分析图像对象的圆形度和面积以及胶原蛋白质量损失对其进行建模。这组成像技术、机器学习程序和统计工具使我们能够在胶原蛋白在骨再生任务中充当支架时，对I型胶原蛋白的生物降解进行表征和参数化。具体而言，参数逻辑混合模型提供了一种识别和建模由于生物活性导致的降解并从而估计相应质量损失比例的方法。此外，所提出的方法可以帮助从光学显微镜获得的信息中估计支架的降解程度。
