MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images. (September 2021)

• Record Type: Journal Article
• Authors: Casado-García, Ángela; Chichón, Gabriela; Domínguez, César; García-Domínguez, Manuel; Heras, Jónathan; Inés, Adrián; López, María; Mata, Eloy; Pascual, Vico; Sáenz, Yolanda
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: Biofilm -- Classification -- Deep learning -- Infectious diseases -- Motility -- Segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104673
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Surface motility has been regarded as a pathogenicity element since it is essential for disease spreading. We have devised a deep learning pipeline for automatically measuring the bacteria spread. The pipeline consists of a classification model and a segmentation model. The results produced by our pipeline are indistinguishable from those produced by experts. We have released our algorithms and models in an open-source set of tools called MotilityJ.