计算机科学
对比度(视觉)
染色
人工智能
水准点(测量)
显微镜
数据科学
病理
医学
大地测量学
地理
作者
Lucas Kreiß,Shaowei Jiang,Xiang Li,Shiqi Xu,Kevin C. Zhou,Kyung Chul Lee,Alexander Mühlberg,Kanghyun Kim,Amey Chaware,D. Michael Ando,Laura Barisoni,Seung Ah Lee,Guoan Zheng,Kyle Lafata,Oliver Friedrich,Roarke Horstmeyer
出处
期刊:PhotoniX
[Springer Nature]
日期:2023-10-10
卷期号:4 (1)
被引量:1
标识
DOI:10.1186/s43074-023-00113-4
摘要
Abstract 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.
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