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-tobackground ratio-all innate properties of imaging in the optical-phase domain. V C 2017 International Society for Advancement of Cytometry

Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges

“…CellProfiler is designed for flexible cell image analysis. To effectively identify clumped cells, CellProfiler contains a modular three‐step strategy based on basic image processing algorithms, including watershed.…”

“…Alanazi's method consists of max‐entropy thresholding and watershed algorithms. High segmentation rates are achieved by employing the optical‐phase metric of the transmitted wave‐front rather than its intensity.…”

“…It has taken the community many decades and great endeavor to segment, identify, and analyze cells for explaining the cellular and molecular processes of health and disease. Studies have showed that cell segmentation can contribute to the better cell recognition in various fields of cell biology (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and great advances have been achieved. However, it still has a long way to go before meeting the ultimate goal that the developed method can segment and identify different types of cells autonomously and the biologists can trust the segmentation and identification results blindly.…”

“…However, most state‐of‐the‐art methods or software tools mainly rely on existing basic image processing algorithms developed many decades ago. For example, the watershed algorithm is adopted by Cellsegm , SMASH , ImageJ , CellProfiler , Alanazi's method , and the recently reported method by Tsujikawa et al (CYTOA 95:4; pp 389–398). Classical thresholding is adopted by Cellsegm, SMASH, ImageJ, and Alanazi's method while K ‐means clustering is adopted by Tsujikawa's method.…”

Three-dimensional label-free observation of individual bacteria upon antibiotic treatment using optical diffraction tomography

“…Previously, 2D QPI techniques have been applied for the study of individual bacteria [54], [55,56]. However, 2D QPI only measures the optical phase delay maps, an integration of RI values along an optical path, and does not provide tomographic measurements of individual bacteria.…”