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细胞和粒子追踪方法。

Methods for cell and particle tracking.

作者信息

Meijering Erik, Dzyubachyk Oleh, Smal Ihor

机构信息

Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.

出版信息

Methods Enzymol. 2012;504:183-200. doi: 10.1016/B978-0-12-391857-4.00009-4.

Abstract

Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images.

摘要

要全面了解任何生物,不可避免地需要对其解剖学和动态特性进行深入分析。为此进行的活细胞成像实验通常会产生大量的延时图像数据,其中包含的信息远远超过人类观察者能够处理的范围。计算机化图像分析提供了以高效且可重复的方式充分利用现有数据的潜力。在许多实验中,一个反复出现的任务是跟踪大量细胞或粒子,并分析它们的(形态)动态行为。在过去十年中,为此目的开发了许多方法,基于这些方法的软件工具也越来越多。在这里,我们综述了该领域的最新进展,并讨论了用于延时显微镜图像中细胞和粒子跟踪及运动分析的各种计算方法、软件工具和定量指标。

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