Digital staining in optical microscopy using deep learning - a review

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 ne...

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Bibliographic Details
Published in:PhotoniX 2023-12, Vol.4 (1), p.34-32, Article 34
Main Authors: Kreiss, Lucas, Jiang, Shaowei, Li, Xiang, Xu, Shiqi, Zhou, Kevin C., Lee, Kyung Chul, Mühlberg, Alexander, Kim, Kanghyun, Chaware, Amey, Ando, Michael, Barisoni, Laura, Lee, Seung Ah, Zheng, Guoan, Lafata, Kyle J., Friedrich, Oliver, Horstmeyer, Roarke
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Cell culture
Contrast agents
Deep learning
Digital staining
Engineering
Histopathology
In-silica
Machine learning
Microscopy
Microwaves
Optical microscopy
Pseudo-H& E
Review
RF and Optical Engineering
Stains & staining
Virtual staining
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Summary: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, 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.
ISSN:2662-1991
2662-1991
DOI:10.1186/s43074-023-00113-4