Aaron Jesse, Wait Eric, DeSantis Michael, Chew Teng-Leong
Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia.
Light Microscopy Facility, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia.
Curr Protoc Cell Biol. 2019 Jun;83(1):e88. doi: 10.1002/cpcb.88. Epub 2019 May 3.
The rapid advancement of live-cell imaging technologies has enabled biologists to generate high-dimensional data to follow biological movement at the microscopic level. Yet, the "perceived" ease of use of modern microscopes has led to challenges whereby sub-optimal data are commonly generated that cannot support quantitative tracking and analysis as a result of various ill-advised decisions made during image acquisition. Even optimally acquired images often require further optimization through digital processing before they can be analyzed. In writing this article, we presume our target audience to be biologists with a foundational understanding of digital image acquisition and processing, who are seeking to understand the essential steps for particle/object tracking experiments. It is with this targeted readership in mind that we review the basic principles of image-processing techniques as well as analysis strategies commonly used for tracking experiments. We conclude this technical survey with a discussion of how movement behavior can be mathematically modeled and described. © 2019 by John Wiley & Sons, Inc.
活细胞成像技术的迅速发展使生物学家能够生成高维数据,以便在微观层面追踪生物运动。然而,现代显微镜“看似”易于使用,却带来了一些挑战,由于在图像采集过程中做出了各种欠妥的决定,通常会生成次优数据,无法支持定量追踪和分析。即使是最佳采集的图像,在进行分析之前,往往也需要通过数字处理进一步优化。在撰写本文时,我们假定目标读者是对数字图像采集和处理有基本了解的生物学家,他们希望了解粒子/物体追踪实验的基本步骤。正是出于这一目标读者群体的考虑,我们回顾了图像处理技术的基本原理以及追踪实验常用的分析策略。我们在本次技术综述的结尾讨论了如何对运动行为进行数学建模和描述。© 2019 约翰威立国际出版公司