Ms. Sumeyra Vural Kaymaz Profile

Sumeyra Vural Kaymaz

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Proceedings Article | 28 September 2023 Presentation + Paper

U-Net–based segmentation of Escherichia coli cells in the absence and presence of ciprofloxacin exposure

Ozge Umut, Miray Esra Palaz, Deniz Muratli, Aleyna Kizilyer, Sumeyra Vural-Kaymaz, Tugce Ermis, Meltem Elitas

Proceedings Volume 12655, 1265502 (2023) https://doi.org/10.1117/12.2677402

KEYWORDS: Image segmentation, Feature extraction, Education and training, Bacteria, Algorithm development, Image processing, Image analysis, Microscopy, Cell phenotyping

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Modern deep learning-based cell segmentation algorithms with high computational power have enabled automated and high-throughput segmentation of bacteria. Previous studies, relying on either manual or automated cell segmentation approaches, have proved that a clonal bacteria population cultured in a regular growth medium in a homogenous microenvironment exhibits heterogeneity. When antibiotic treatment has been applied, heterogeneity of the bacteria population may increase depending on the working mechanisms of an administrated antibiotic. Therefore, important features of rare cells, such as asymmetric division of antibiotic persister cells or cells with metastable phenotypes, might be masked by heterogeneity of a population, particularly when a limited number of the cells was analyzed. Therefore, automated image segmentation and analysis approaches have significantly impact on accurate, rapid, and reliable feature identification, particularly for extracting quantitative high-resolution data at high throughput. Here, we implemented U-Net algorithm for segmentation of Escherichia coli cells in the absence and presence of ciprofloxacin. The accuracy values were 0.9912 and 0.9869 for the control and ciprofloxacin-treated cell populations, respectively. Next, we developed an algorithm using Phyton and the OpenCV library to extract the cell number, cellular area, and solidity features of the cells. We believe that our preliminary data might contribute to development of automated, reliable, accurate, and bacteria or antibiotic specific image segmentation tools.

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