Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform

With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed...

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Bibliographic Details
Published in:Journal of the Royal Society interface 2017-02, Vol.14 (127), p.20160705-20160705
Main Authors: Versari, Cristian, Stoma, Szymon, Batmanov, Kirill, Llamosi, Artémis, Mroz, Filip, Kaczmarek, Adam, Deyell, Matt, Lhoussaine, Cédric, Hersen, Pascal, Batt, Gregory
Format: Article
Language:English
Subjects:
Benchmarks
Bioinformatics
Cell Tracking - instrumentation
Cell Tracking - methods
Computer Science
Evaluation
Graphical user interface
Image Analysis
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging Benchmark
Life Sciences–Engineering interface
Microscopy
Parameter Learning
Saccharomyces cerevisiae
Schizosaccharomyces - cytology
Segmentation And Tracking
Tracking
Yeast
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Summary:With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.
ISSN:1742-5689
1742-5662
DOI:10.1098/rsif.2016.0705