تقرير
Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images
| العنوان: | Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images |
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| المؤلفون: | Hickl, Vincent, Khan, Abid, Rossi, René M., Silva, Bruno F. B., Maniura-Weber, Katharina |
| سنة النشر: | 2024 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Biological Physics, Physics - Medical Physics |
| الوصف: | 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. Comment: 12 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.12407 |
| رقم الأكسشن: | edsarx.2405.12407 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |