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An Algorithm to Automate Yeast Segmentation and Tracking

Author

Listed:
  • Andreas Doncic
  • Umut Eser
  • Oguzhan Atay
  • Jan M Skotheim

Abstract

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

Suggested Citation

  • Andreas Doncic & Umut Eser & Oguzhan Atay & Jan M Skotheim, 2013. "An Algorithm to Automate Yeast Segmentation and Tracking," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-11, March.
    • Handle: RePEc:plo:pone00:0057970
    DOI: 10.1371/journal.pone.0057970
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    References listed on IDEAS

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    3. Stefano Di Talia & Jan M. Skotheim & James M. Bean & Eric D. Siggia & Frederick R. Cross, 2007. "The effects of molecular noise and size control on variability in the budding yeast cell cycle," Nature, Nature, vol. 448(7156), pages 947-951, August.
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