Digital staining in optical microscopy using deep learning-a review  

作　　者：Lucas Kreiss Shaowei Jiang Xiang Li Shiqi Xu Kevin C.Zhou Kyung Chul Lee Alexander Mühlberg Kanghyun Kim Amey Chaware Michael Ando Laura Barisoni Seung Ah Lee Guoan Zheng Kyle J.Lafata Oliver Friedrich Roarke Horstmeyer 

机构地区：[1]Department of Biomedical Engineering,Duke University,Durham,NC 27708,USA [2]Institute of Medical Biotechnology,Friedrich-Alexander University(FAU),Erlangen,Germany [3]Department of Biomedical Engineering,University of Connecticut,Mansfield,Connecticut,USA [4]Department of Radiation Physics,Duke University,Durham,NC 27708,USA [5]Department of Electrical Engineering&Computer Sciences,University of California,Berkeley,CA,USA [6]School of Electrical&Electronic Engineering,Yonsei University,Seoul 03722,Republic of Korea [7]Google,Inc.,Mountain View,CA 94043,USA [8]Department of Pathology,Duke University,Durham,NC 27708,USA

出　　处：《PhotoniX》2023年第1期64-95,共32页智汇光学（英文）

基　　金：This project has received funding from the European Union’s Horizon 2022 Marie Skłodowska-Curie Action(grant agreement 101103200,‘MICS’to L.K.);K.C.Z.was supported in part by Schmidt Science Fellows,in partnership with the Rhodes Trust;K.C.L.was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(grant number:HI21C0977060102002);Commercialization Promotion Agency for R&D Outcomes(COMPA)funded by the Ministry of Science and ICT(MSIT)(1711198540);This material is based upon work supported in part by the Air Force Office of Scientific Research under award number FA9550-21-1-0401,the National Science Foundation under Grant 2238845,and a Hartwell Foundation Individual Biomedical Researcher Award.

摘　　要：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.

关 键 词：Deep learning Digital staining Optical microscopy Virtual staining In-silica Pseudo-H&E Virtual fluorescence Generative models Image translation 

分 类 号：TH742[机械工程—光学工程]