Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | MOLECULAR IMAGING AND BIOLOGY |
ISSN | 1536-1632 |
2020-06-08 | |
期号 | 22页码:1301-1309 |
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. |
关键词 | Virtual histological staining Conditional generative adversarial network Blind evaluation Bright-field microscopic imaging |
DOI | 10.1007/s11307-020-01508-6 |
关键词[WOS] | Virtual histological staining ; Conditional generative adversarial network ; Blind evaluation ; Bright-field microscopic imaging |
收录类别 | SCIE |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000538971900003 |
出版者 | SPRINGER |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39794 |
专题 | 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.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 |
第一作者单位 | 中国科学院分子影像重点实验室 |
通讯作者单位 | 中国科学院分子影像重点实验室 |
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(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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