Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917891864002560 |
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| author | Isil, Cagatay Koydemir, Hatice Ceylan Eryilmaz, Merve de Haan, Kevin Pillar, Nir Mentesoglu, Koray Unal, Aras Firat Rivenson, Yair Chandrasekaran, Sukantha Garner, Omai B. Ozcan, Aydogan |
| author_facet | Isil, Cagatay Koydemir, Hatice Ceylan Eryilmaz, Merve de Haan, Kevin Pillar, Nir Mentesoglu, Koray Unal, Aras Firat Rivenson, Yair Chandrasekaran, Sukantha Garner, Omai B. Ozcan, Aydogan |
| contents | Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained deep neural network that digitally transforms darkfield images of unstained bacteria into their Gram-stained equivalents matching brightfield image contrast. After a one-time training effort, the virtual Gram staining model processes an axial stack of darkfield microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of the virtual Gram staining workflow on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the virtual Gram staining model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacteria staining framework effectively bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12337 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning Isil, Cagatay Koydemir, Hatice Ceylan Eryilmaz, Merve de Haan, Kevin Pillar, Nir Mentesoglu, Koray Unal, Aras Firat Rivenson, Yair Chandrasekaran, Sukantha Garner, Omai B. Ozcan, Aydogan Quantitative Methods Machine Learning Image and Video Processing Medical Physics Optics Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained deep neural network that digitally transforms darkfield images of unstained bacteria into their Gram-stained equivalents matching brightfield image contrast. After a one-time training effort, the virtual Gram staining model processes an axial stack of darkfield microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of the virtual Gram staining workflow on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the virtual Gram staining model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacteria staining framework effectively bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations. |
| title | Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning |
| topic | Quantitative Methods Machine Learning Image and Video Processing Medical Physics Optics |
| url | https://arxiv.org/abs/2407.12337 |