Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917726209966080 |
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| author | Hickl, Vincent Khan, Abid Rossi, René M. Silva, Bruno F. B. Maniura-Weber, Katharina |
| author_facet | Hickl, Vincent Khan, Abid Rossi, René M. Silva, Bruno F. B. Maniura-Weber, Katharina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images Hickl, Vincent Khan, Abid Rossi, René M. Silva, Bruno F. B. Maniura-Weber, Katharina Biological Physics Medical Physics 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. |
| title | Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images |
| topic | Biological Physics Medical Physics |
| url | https://arxiv.org/abs/2405.12407 |