Segmentation of dense and multi-species bacterial colonies achieved using models trained on synthetic microscopy images
arxiv(2024)
Abstract
The spread of microbial infections is governed by the self-organization of
bacteria on surfaces. Limitations of live imaging techniques make collective
behaviors in clinically relevant systems challenging to quantify. Here, novel
experimental and image analysis techniques for high-fidelity single-cell
segmentation of bacterial colonies are developed. Machine learning-based
segmentation models are trained solely using synthetic microscopy images that
are processed to look realistic using state-of-the-art image-to-image
translation methods, requiring no biophysical modeling. Accurate single-cell
segmentation is achieved for densely packed single-species colonies and
multi-species colonies of common pathogenic bacteria, even under suboptimal
imaging conditions and for both brightfield and confocal laser scanning
microscopy. The resulting data provide quantitative insights into the
self-organization of bacteria on soft surfaces. Thanks to their high
adaptability and relatively simple implementation, these methods promise to
greatly facilitate quantitative descriptions of bacterial infections in varied
environments.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined