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U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting.

Libo Cao1, Liping Zeng2, Yaoxuan Wang1

  • 1Center for Global Health, Nanjing Medical University, Nanjing 211166, China.

Microorganisms
|January 23, 2024
PubMed
Summary

This study introduces an automated system for counting microbial colonies using advanced image processing and convolutional neural networks (CNNs). The novel method achieves high accuracy in colony counting and adhesion classification, enhancing laboratory efficiency.

Keywords:
ResNet50U2-Netbacterial colony countingconvolutional neural networksimage segmentationimage spatial normalizationlight intensity correction

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Area of Science:

  • Microbiology
  • Computer Vision
  • Bioinformatics

Background:

  • Accurate microbial colony counting is crucial for various laboratory applications.
  • Manual counting is labor-intensive, prone to errors, and time-consuming.
  • Automated systems can improve efficiency and reproducibility in microbial quantification.

Purpose of the Study:

  • To develop an automated colony counting system using improved image preprocessing and convolutional neural network (CNN) assistance.
  • To enhance the accuracy and automation of recognizing and counting single and multi-colony targets in laboratory cultures.
  • To validate the system's performance in colony counting and adhesion classification.

Main Methods:

  • An LED backlighting illumination platform was used for image acquisition of agar plate cultures.
  • An image preprocessing algorithm based on light intensity correction was implemented for clearer colony-media differentiation.
  • U2-Net models were employed for segmenting the Petri dish edge and the colony region.
  • ResNet50 was utilized for the final automatic counting of segmented colony components.

Main Results:

  • The U2-Net for Petri dish edge detection achieved an F1 score of 99.5% and MAE of 0.0033.
  • The U2-Net for colony region segmentation achieved an F1 score of 96.5% and MAE of 0.005.
  • The overall colony counting recovery rate was 97.82%, with excellent performance in adhesion classification.

Conclusions:

  • The developed automated system demonstrates high accuracy and automation for microbial colony counting.
  • The proposed pipeline, integrating light intensity correction, U2-Net segmentation, and ResNet50 counting, is a novel approach.
  • This system offers a significant advancement for quantitative microbiology research and diagnostics.