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

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 seg-mentation, 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.
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Cristian Versari, Szymon Stoma, Kirill Batmanov, Artémis Llamosi, Filip Mroz, et al.. Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform. Journal of the Royal Society Interface, the Royal Society, 2017, 14 (127), pp.32. ⟨10.1098/rsif.2016.0705⟩. ⟨hal-01547623⟩

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