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Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue

文献类型：期刊论文

作者Zhang, Guanghao1,6; Ning, Bin5; Hui, Hui1,4; Yu, Tengfei5; Yang, Xin1,4; Zhang, Hongxia5; Tian, Jie1,2,3; He, Wen5
刊名MOLECULAR IMAGING AND BIOLOGY
出版日期2021-10-07
页码11
关键词Multiple virtual histological staining Pix2pix network Human carotid atheroma Blind evaluation Bright-field microscopic imaging
ISSN号1536-1632
DOI10.1007/s11307-021-01641-w
通讯作者Tian, Jie(tian@ieee.org) ; He, Wen(hewen@bjtth.org)
英文摘要Purpose Histological analysis of human carotid atherosclerotic plaques is critical in understanding atherosclerosis biology and developing effective plaque prevention and treatment for ischemic stroke. However, the histological staining process is laborious, tedious, variable, and destructive to the highly valuable atheroma tissue obtained from patients. Procedures We proposed a deep learning-based method to simultaneously transfer bright-field microscopic images of unlabeled tissue sections into equivalent multiple sections of the same samples that are virtually stained. Using a pix2pix model, we trained a generative adversarial neural network to achieve image-to-images translation of multiple stains, including hematoxylin and eosin (H&E), picrosirius red (PSR), and Verhoeff van Gieson (EVG) stains. Results The quantification of evaluation metrics indicated that the proposed approach achieved the best performance in comparison with other state-of-the-art methods. Further blind evaluation by board-certified pathologists demonstrated that the multiple virtual stains have high consistency with standard histological stains. The proposed approach also indicated that the generated histopathological features of atherosclerotic plaques, such as the necrotic core, neovascularization, cholesterol crystals, collagen, and elastic fibers, are optimally matched with those of standard histological stains. Conclusions The proposed approach allows for the virtual staining of unlabeled human carotid plaque tissue images with multiple types of stains. In addition, it identifies the histopathological features of atherosclerotic plaques in the same tissue sample, which could facilitate the development of personalized prevention and other interventional treatments for carotid atherosclerosis.
WOS关键词MICROSCOPY ; GENERATION
资助项目National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Natural Science Foundation of China[81730050] ; National Natural Science Foundation of China[81827808] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; CAS Youth Innovation Promotion Association[2018167] ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; CAS Scientific Instrument RD Program[YJKYYQ20170075]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000705713900001
出版者SPRINGER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; CAS Youth Innovation Promotion Association ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; CAS Scientific Instrument RD Program
源URL[http://ir.ia.ac.cn/handle/173211/46176]  
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Tian, Jie; He, Wen
作者单位1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai 519000, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100083, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Capital Med Univ, Beijing Tiantan Hosp, Dept Ultrasound, Beijing 100070, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Guanghao,Ning, Bin,Hui, Hui,et al. Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue[J]. MOLECULAR IMAGING AND BIOLOGY,2021:11.
APA Zhang, Guanghao.,Ning, Bin.,Hui, Hui.,Yu, Tengfei.,Yang, Xin.,...&He, Wen.(2021).Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue.MOLECULAR IMAGING AND BIOLOGY,11.
MLA Zhang, Guanghao,et al."Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue".MOLECULAR IMAGING AND BIOLOGY (2021):11.

入库方式： OAI收割

来源：自动化研究所

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