CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue
Li, Dan1,2; Hui, Hui2,3; Zhang, Yingqian4; Tong, Wei4; Tian, Feng4; Yang, Xin2; Liu, Jie1; Chen, Yundai4; Tian, Jie2,3,5
Source PublicationMOLECULAR IMAGING AND BIOLOGY
ISSN1536-1632
2020-06-08
Issue22Pages:1301-1309
Abstract

Purpose Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histological image analysis. Procedures In this study, we developed a deep learning-based method to transfer bright-field microscopic images of unlabeled tissue sections into equivalent bright-field images of histologically stained versions of the same samples. We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model. Results The results of a blind evaluation by board-certified pathologists illustrate that the virtual staining and standard histological staining images of rat carotid artery tissue sections and those involving different types of stains showed no major differences. Quantification of virtual and histological H&E staining in carotid artery tissue sections showed that the relative errors of intima thickness, intima area, and media area were lower than 1.6 %, 5.6 %, and 12.7 %, respectively. The training time of deep learning network was 12.857 h with 1800 training patches and 200 epoches. Conclusions This virtual staining method significantly mitigates the typically laborious and time-consuming histological staining procedures and could be augmented with other label-free microscopic imaging modalities.

KeywordVirtual histological staining Conditional generative adversarial network Blind evaluation Bright-field microscopic imaging
DOI10.1007/s11307-020-01508-6
WOS KeywordVirtual histological staining ; Conditional generative adversarial network ; Blind evaluation ; Bright-field microscopic imaging
Indexed BySCIE
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81827808] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81571836] ; National Natural Science Foundation of China[81800221] ; National Natural Science Foundation of China[81227901] ; Scientific Instrument R&D Program of the Chinese Academy of Sciences[YJKYYQ20170075] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32030200]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Scientific Instrument R&D Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000538971900003
PublisherSPRINGER
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39794
Collection中国科学院分子影像重点实验室
Corresponding AuthorLiu, Jie; Chen, Yundai; Tian, Jie
Affiliation1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Dept Biomed Engn, Beijing 100044, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Beijing 100853, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100083, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Dan,Hui, Hui,Zhang, Yingqian,et al. Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue[J]. MOLECULAR IMAGING AND BIOLOGY,2020(22):1301-1309.
APA Li, Dan.,Hui, Hui.,Zhang, Yingqian.,Tong, Wei.,Tian, Feng.,...&Tian, Jie.(2020).Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.MOLECULAR IMAGING AND BIOLOGY(22),1301-1309.
MLA Li, Dan,et al."Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue".MOLECULAR IMAGING AND BIOLOGY .22(2020):1301-1309.
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