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Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue

文献类型：期刊论文

作者Li, Dan3,4; Hui, Hui4,5; Zhang, Yingqian1; Tong, Wei1; Tian, Feng1; Yang, Xin4; Liu, Jie3; Chen, Yundai1; Tian, Jie2,4,5
刊名MOLECULAR IMAGING AND BIOLOGY
出版日期2020-06-08
页码9
关键词Virtual histological staining Conditional generative adversarial network Blind evaluation Bright-field microscopic imaging
ISSN号1536-1632
DOI10.1007/s11307-020-01508-6
通讯作者Liu, Jie(jieliu@bjtu.edu.cn) ; Chen, Yundai(cyundai@vip.163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要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.
WOS关键词PLAQUE PROGRESSION ; GENERATION
资助项目National 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]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000538971900003
出版者SPRINGER
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/39793]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Liu, Jie; Chen, Yundai; Tian, Jie
作者单位1.Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Beijing 100853, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100083, Peoples R China
3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Dept Biomed Engn, Beijing 100044, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
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,2020:9.
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,9.
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 (2020):9.

入库方式： OAI收割

来源：自动化研究所

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