METHODS FOR CELL AND PARTICLE TRACKING

被引：1081

作者：

Meijering, Erik ^{[1
,2
]}

Dzyubachyk, Oleh ^{[1
,2
]}

Smal, Ihor ^{[1
,2
]}

机构：

[1] Erasmus MC Univ Med Ctr Rotterdam, Biomed Imaging Grp Rotterdam, Dept Med Informat, Rotterdam, Netherlands

[2] Erasmus MC Univ Med Ctr Rotterdam, Biomed Imaging Grp Rotterdam, Dept Radiol, Rotterdam, Netherlands

来源：

IMAGING AND SPECTROSCOPIC ANALYSIS OF LIVING CELLS: OPTICAL AND SPECTROSCOPIC TECHNIQUES | 2012年 / 504卷

关键词：

BIOIMAGE INFORMATICS; AUTOMATIC TRACKING; MICROSCOPY; QUANTIFICATION; MOTILITY; IMAGES;

D O I：

10.1016/B978-0-12-391857-4.00009-4

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 timelapse 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.

引用

页码：183 / 200

页数：18

共 45 条

[1] 4-D single particle tracking of synthetic and proteinaceous microspheres reveals preferential movement of nuclear particles along chromatin-poor tracks [J].