Deep learning-enabled virtual histological staining of biological samples

作     者：Bijie Bai Xilin Yang Yuzhu Li Yijie Zhang Nir Pillar Aydogan Ozcan Bijie Bai;Xilin Yang;Yuzhu Li;Yijie Zhang;Nir Pillar;Aydogan Ozcan

作者机构：Electrical and Computer Engineering DepartmentUniversity of CaliforniaLos AngelesCA 90095USA Bioengineering DepartmentUniversity of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSi)University of CaliforniaLos AngelesCAUSA 

出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)

年 卷 期：2023年第12卷第3期

页      面：335-354页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The Ozcan Research Group at UCLA acknowledges the support of the NSF Biophotonics Program. 

主　　题：Deep generating consuming 

摘      要：Histological staining is the gold standard for tissue examination in clinical pathology and life-science research,which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue.However,the current histological staining workflow requires tedious sample preparation steps,specialized laboratory infrastructure,and trained histotechnologists,making it expensive,time-consuming,and not accessible in resource-limited settings.Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks,providing rapid,cost-effective,and accurate alternatives to standard chemical staining methods.These techniques,broadly referred to as virtual staining,were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples;similar approaches were also used for transforming images of an already stained tissue sample into another type of stain,performing virtual stain-to-stain transformations.In this Review,we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques.The basic concepts and the typical workflow of virtual staining are introduced,followed by a discussion of representative works and their technical innovations.We also share our perspectives on the future of this emerging field,aiming to inspire readers from diverse scientifc fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.