Digital staining in optical microscopy using deep learning -- a review
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916299436720128 |
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| author | Kreiss, Lucas Jiang, Shaowei Li, Xiang Xu, Shiqi Zhou, Kevin C. Mühlberg, Alexander Lee, Kyung Chul Kim, Kanghyun Chaware, Amey Ando, Michael Barisoni, Laura Lee, Seung Ah Zheng, Guoan Lafata, Kyle Friedrich, Oliver Horstmeyer, Roarke |
| author_facet | Kreiss, Lucas Jiang, Shaowei Li, Xiang Xu, Shiqi Zhou, Kevin C. Mühlberg, Alexander Lee, Kyung Chul Kim, Kanghyun Chaware, Amey Ando, Michael Barisoni, Laura Lee, Seung Ah Zheng, Guoan Lafata, Kyle Friedrich, Oliver Horstmeyer, Roarke |
| contents | Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08140 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Digital staining in optical microscopy using deep learning -- a review Kreiss, Lucas Jiang, Shaowei Li, Xiang Xu, Shiqi Zhou, Kevin C. Mühlberg, Alexander Lee, Kyung Chul Kim, Kanghyun Chaware, Amey Ando, Michael Barisoni, Laura Lee, Seung Ah Zheng, Guoan Lafata, Kyle Friedrich, Oliver Horstmeyer, Roarke Image and Video Processing Machine Learning Biological Physics Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications. |
| title | Digital staining in optical microscopy using deep learning -- a review |
| topic | Image and Video Processing Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2303.08140 |