Robust Microbial Cell Segmentation by Quantitative Phase Imaging.

ORAL

Abstract

Cell imaging is an important tool in cell and molecular biology research. This is because such procedures allow us to determine the cellular function-structure relationship. However, the first step to extract cell physiology information from all forms of biological imaging experiments is to segment cells. As such, cell segmentation has attracted considerable attention in computational image processing. Despite such progress however, it remains challenging to identify a global algorithm that pertains to all cellular models ranging from mammalian lines to yeast and bacteria. To address this shortcoming, we undertook a different approach by replacing conventional imaging modalities, such as phase contrast and fluorescence with Quantitative Phase Imaging (QPI). QPI relies on the optical-phase rather than intensity to image cells and localize their contour. In this way, QPI enabled a very high cell segmentation success-rate greater than 99{\%} for yeast and 98{\%} for E. coli bacteria cells without any computationally intensive, post-acquisition processing. 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.

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Authors

  • Hamdah Alanazi

    University of Idaho

  • Kathryn Hadley

    University of Idaho, Oregon State University, National Renewable Energy Laboratory, Golden, CO 80401, Cornell University, Ithaca, New York 14853, University of Florida Gainesville, FL 32611, Univ of Puget Sound, UNIST, St. Mary's University of Minnesota, Winona State University, Choice Research Group, Western Washington University, University of British Columbia - Okanagan, Center for Cancer Research, National Cancer Institute (NCI)

  • Kathryn Hadley

    University of Idaho, Oregon State University, National Renewable Energy Laboratory, Golden, CO 80401, Cornell University, Ithaca, New York 14853, University of Florida Gainesville, FL 32611, Univ of Puget Sound, UNIST, St. Mary's University of Minnesota, Winona State University, Choice Research Group, Western Washington University, University of British Columbia - Okanagan, Center for Cancer Research, National Cancer Institute (NCI)

  • Kathryn Hadley

    University of Idaho, Oregon State University, National Renewable Energy Laboratory, Golden, CO 80401, Cornell University, Ithaca, New York 14853, University of Florida Gainesville, FL 32611, Univ of Puget Sound, UNIST, St. Mary's University of Minnesota, Winona State University, Choice Research Group, Western Washington University, University of British Columbia - Okanagan, Center for Cancer Research, National Cancer Institute (NCI)

  • Kathryn Hadley

    University of Idaho, Oregon State University, National Renewable Energy Laboratory, Golden, CO 80401, Cornell University, Ithaca, New York 14853, University of Florida Gainesville, FL 32611, Univ of Puget Sound, UNIST, St. Mary's University of Minnesota, Winona State University, Choice Research Group, Western Washington University, University of British Columbia - Okanagan, Center for Cancer Research, National Cancer Institute (NCI)