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

doi:10.1007/s11307-021-01641-w

	Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue
	Zhang, Guanghao 1,2; Ning, Bin 3; Hui, Hui2,4 ; Yu, Tengfei 3; Yang, Xin2,4 ; Zhang, Hongxia 3; Tian, Jie 2,5,6; He, Wen 3
发表期刊	MOLECULAR IMAGING AND BIOLOGY
ISSN	1536-1632
	2021-10-07
页码	11
通讯作者	Tian, Jie(tian@ieee.org) ; He, Wen(hewen@bjtth.org)
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.
关键词	Multiple virtual histological staining Pix2pix network Human carotid atheroma Blind evaluation Bright-field microscopic imaging
DOI	10.1007/s11307-021-01641-w
关键词[WOS]	MICROSCOPY ; GENERATION
收录类别	SCI
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 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]
项目资助者	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
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000705713900001
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	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46177
专题	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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Capital Med Univ, Beijing Tiantan Hosp, Dept Ultrasound, Beijing 100070, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100083, Peoples R China 6.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai 519000, 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	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.