Nketia Thomas A, Sailem Heba, Rohde Gustavo, Machiraju Raghu, Rittscher Jens
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom.
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States.
Methods. 2017 Feb 15;115:65-79. doi: 10.1016/j.ymeth.2017.02.007. Epub 2017 Feb 27.
Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits.
光学显微镜、生物传感器和细胞培养技术的进步改变了活细胞成像。得益于这些进展，活细胞成像在基础生物学研究以及药物开发的各个阶段都发挥着越来越重要的作用。需要图像分析方法从这些庞大而复杂的数据集中提取定量信息。本综述的目的是概述用于活细胞成像的可用图像分析方法，特别是所需的预处理、图像分割、细胞跟踪和数据可视化方法。还将讨论机器学习，尤其是深度学习和计算机视觉的最新进展所带来的潜在机遇。本综述包括对不同可用软件包和工具包的概述。
