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

doi:10.1117/12.2677402

28 September 2023 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

Author Affiliations +

Proceedings Volume 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; 1265502 (2023) https://doi.org/10.1117/12.2677402
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ozge Umut, Miray Esra Palaz, Deniz Muratli, Aleyna Kizilyer, Sumeyra Vural-Kaymaz, Tugce Ermis, and Meltem Elitas "U-Net–based segmentation of Escherichia coli cells in the absence and presence of ciprofloxacin exposure", Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 1265502 (28 September 2023); https://doi.org/10.1117/12.2677402

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available