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

Open access
Date
2017-02Type
- Journal Article
Citations
Cited 33 times in
Web of Science
Cited 36 times in
Scopus
ETH Bibliography
yes
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Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000129641Publication status
publishedExternal links
Journal / series
Journal of the Royal Society. InterfaceVolume
Pages / Article No.
Publisher
Royal SocietySubject
Image analysis; Segmentation and tracking; Parameter learning; Imaging benchmarkOrganisational unit
02891 - ScopeM / ScopeM
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Citations
Cited 33 times in
Web of Science
Cited 36 times in
Scopus
ETH Bibliography
yes
Altmetrics