Related Experiment Video
Updated: Oct 26, 2025
Visualizing Bacterial Motility Based on a Color Reaction
Published on: February 15, 2022
MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images
Ángela Casado-García1, Gabriela Chichón2, César Domínguez1
1Department of Mathematics and Computer Science, University of La Rioja, Spain.
Background And Objectives:
Infectious diseases produced by antimicrobial resistant microorganisms are a major threat to human, and animal health worldwide. This problem is increased by the virulence and spread of these bacteria. Surface motility has been regarded as a pathogenicity element because it is essential for many biological functions, but also for disease spreading; hence, investigations on the motility behaviour of bacteria are crucial to understand chemotaxis, biofilm formation and virulence in general. To identify a motile strain in the laboratory, the bacterial spread area is observed on media solidified with agar. Up to now, the task of measuring bacteria spread was a manual, and, therefore, tedious and time-consuming task. The aim of this work is the development of a set of tools for bacteria segmentation in motility images.
Methods:
In this work, we address the problem of measuring bacteria spread on motility images by creating an automatic pipeline based on deep learning models. Such a pipeline consists of a classification model to determine whether the bacteria has spread to cover completely the Petri dish, and a segmentation model to determine the spread of those bacteria that do not fully cover the Petri dishes. In order to annotate enough images to train our deep learning models, a semi-automatic annotation procedure is presented.
Results:
The classification model of our pipeline achieved a F1-score of 99.85%, and the segmentation model achieved a Dice coefficient of 95.66%. In addition, the segmentation model produces results that are indistinguishable, and in many cases preferred, from those produced manually by experts. Finally, we facilitate the dissemination of our pipeline with the development of MotilityJ, an open-source and user-friendly application for measuring bacteria spread on motility images.
Conclusions:
In this work, we have developed an algorithm and trained several models for measuring bacteria spread on motility images. Thanks to this work, the analysis of motility images will be faster and more reliable. The developed tools will help to advance our understanding of the behaviour and virulence of bacteria.
Insights
This study introduces an automated deep learning pipeline for analyzing bacterial motility in images, significantly speeding up and improving the reliability of measurements. The developed tools aid in understanding bacterial behavior and virulence.
Area of Science:
- Microbiology
- Computer Science
- Bioinformatics
Background:
- Antimicrobial resistance and bacterial spread pose global health threats.
- Bacterial surface motility is a key factor in virulence and disease dissemination.
- Manual measurement of bacterial spread in motility images is time-consuming and tedious.
Purpose of the Study:
- To develop automated tools for bacteria segmentation in motility images.
- To create a deep learning pipeline for accurate measurement of bacterial spread.
- To enhance the understanding of bacterial behavior and virulence.
Main Methods:
- Developed an automatic pipeline using deep learning models for image analysis.
- Implemented a classification model to detect full Petri dish coverage.
- Utilized a segmentation model for quantifying bacterial spread in incomplete coverage scenarios.
- Introduced a semi-automatic annotation procedure for training data.
Main Results:
- The classification model achieved a 99.85% F1-score.
- The segmentation model achieved a 95.66% Dice coefficient.
- Automated segmentation results were comparable or superior to expert manual analysis.
- Developed MotilityJ, an open-source application for user-friendly analysis.
Conclusions:
- An effective algorithm and deep learning models were developed for measuring bacterial spread.
- The automated analysis significantly improves speed and reliability in motility image analysis.
- The tools contribute to advancing the understanding of bacterial virulence and behavior.
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