title: Semi-automated object tracking methods in biological imaging.
abstract: Time-lapse imaging is a rich data source offering potential kinetic information of cellular activity and behavior. Tracking and extracting measurements of objects from time-lapse datasets are challenges that result from the complexity and dynamics of each object's motion and intensity or the appearance of new objects in the field of view. A wide range of strategies for proper data sampling, object detection, image analysis, and post-analysis interpretation are available. Theory and methods for single-particle tracking, spot detection, and object linking are discussed in this unit, as well as examples with step-by-step procedures for utilizing semi-automated software and visualization tools for achieving tracking results and interpreting this output.
authors.affiliation: Nikon Instruments, Melville, New York.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Davis
authors.affiliation: Laboratory Imaging, Prague, Czech Republic.
authors.forename: Ondřej
authors.initials: O
authors.lastname: Pražský
authors.affiliation: Nikon Instruments, Melville, New York.
authors.forename: Laura R
authors.initials: LR
authors.lastname: Sysko
mesh_terms: D000818:Animals; D001773:Blood Cells / Q000166:cytology; D002696:Chlamydomonas / Q000166:cytology; D005453:Fluorescence; D021621:Imaging, Three-Dimensional; D010363:Pattern Recognition, Automated / Q000379:methods*; D034741:RNA, Small Interfering / Q000378:metabolism; D012039:Regional Blood Flow; D059008:Time-Lapse Imaging; D015027:Zebrafish
chemical_list: D034741:RNA, Small Interfering
keywords: digital imaging; image analysis; image segmentation methods; object linking; object tracking; sampling frequency; single-particle tracking