Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, Georgia, United States of America.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2020 Sep 3;16(9):e1007758. doi: 10.1371/journal.pcbi.1007758. eCollection 2020 Sep.
With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations.
随着生物医学研究中成像数据的质量和数量不断增加，人们对能够高效可靠地自动提取这些图像中包含的定量信息的计算方法提出了需求。提供这种方法的挑战之一是需要根据数据的具体情况对算法进行定制，从而限制了其应用领域。在这里，我们提出了一种广泛适用于生物或其他多组分形成物中复杂形状和模式的定量和分类的方法。这种方法将图像中所有形状边界映射到一个全局信息丰富的图上，并对图的多维度量进行机器学习。我们通过以下方式证明了这种方法的有效性：(1) 在使用内皮管形成测定法的表型挽救实验中，从视觉上无法区分的图像中提取细微的结构差异；(2) 训练算法识别我们的细胞集体行为模拟模型中不同多细胞网络形成的生物物理参数；(3) 分析 U2OS 细胞培养物对广泛的小分子扰动的反应。
