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Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements.


ABSTRACT: High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

SUBMITTER: Alanazi H 

PROVIDER: S-EPMC6585648 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements.

Alanazi H H   Canul A J AJ   Garman A A   Quimby J J   Vasdekis A E AE  

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20170330 5


High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rat  ...[more]

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