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Automated tracking of migrating cells in phase‐contrast video microscopy sequences using image registration
Author(s) -
HAND A.J.,
SUN T.,
BARBER D.C.,
HOSE D.R.,
MACNEIL S.
Publication year - 2009
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2009.03144.x
Subject(s) - tracking (education) , computer vision , artificial intelligence , phase contrast microscopy , computer science , contrast (vision) , tracking system , confocal , microscopy , image registration , software , microscope , video microscopy , image processing , image (mathematics) , optics , physics , biology , psychology , pedagogy , filter (signal processing) , programming language , microbiology and biotechnology
Summary Analysis of in vitro cell motility is a useful tool for assessing cellular response to a range of factors. However, the majority of cell‐tracking systems available are designed primarily for use with fluorescently labelled images. In this paper, five commonly used tracking systems are examined for their performance compared with the use of a novel in‐house cell‐tracking system based on the principles of image registration and optical flow. Image registration is a tool commonly used in medical imaging to correct for the effects of patient motion during imaging procedures and works well on low‐contrast images, such as those found in bright‐field and phase‐contrast microscopy. The five cell‐tracking systems examined were Retrac, a manual tracking system used as the gold standard; CellTrack, a recently released freely downloadable software system that uses a combination of tracking methods; ImageJ, which is a freely available piece of software with a plug‐in for automated tracking (MTrack2) and Imaris and Volocity, both commercially available automated tracking systems. All systems were used to track migration of human epithelial cells over ten frames of a phase‐contrast time‐lapse microscopy sequence. This showed that the in‐house image‐registration system was the most effective of those tested when tracking non‐dividing epithelial cells in low‐contrast images, with a successful tracking rate of 95%. The performance of the tracking systems was also evaluated by tracking fluorescently labelled epithelial cells imaged with both phase‐contrast and confocal microscopy techniques. The results showed that using fluorescence microscopy instead of phase contrast does improve the tracking efficiency for each of the tested systems. For the in‐house software, this improvement was relatively small (<5% difference in tracking success rate), whereas much greater improvements in performance were seen when using fluorescence microscopy with Volocity and ImageJ.