Institut Pasteur, Groupe Imagerie et Modélisation, 75015 Paris, France.
Cell Microbiol. 2012 Dec;14(12):1828-35. doi: 10.1111/cmi.12032. Epub 2012 Nov 1.
Light microscopy offers a unique window into the life and works of microbes and their interactions with hosts. Mere visualization of images, however, does not provide the quantitative information needed to reliably and accurately characterize phenotypes or test computational models of cellular processes, and is unfeasible in high-throughput screens. Algorithms that automatically extract biologically meaningful quantitative data from images are therefore an increasingly essential complement to the microscopes themselves. This paper reviews some of the computational methods developed to detect, segment and track cells, molecules or viruses, with an emphasis on their underlying assumptions, limitations, and the importance of validation.
光学显微镜为观察微生物的生命活动及其与宿主的相互作用提供了独特的窗口。然而，仅仅对图像进行可视化并不能提供可靠和准确地表征表型或测试细胞过程的计算模型所需的定量信息，并且在高通量筛选中也是不可行的。因此，能够自动从图像中提取有生物学意义的定量数据的算法是显微镜本身的一个越来越重要的补充。本文综述了一些用于检测、分割和跟踪细胞、分子或病毒的计算方法，重点介绍了它们的基本假设、局限性以及验证的重要性。
