Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.
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...
| Publicado en: | Molecular Imaging & Biology Vol. 22; no. 5; pp. 1301 - 1310 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145733577&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145733577 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15361632 KJU jtl: Molecular Imaging & Biology issn: 15361632 maglogo: N pubinfo: dt: Oct2020 vid: 22 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145733577 144064507 145733577 NLM32514884 145733577 10.1007/s11307-020-01508-6 NLM32514884 145733577 ppf: 1301 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue. aug: au: Li, Dan Hui, Hui Zhang, Yingqian Tong, Wei Tian, Feng Yang, Xin Liu, Jie Chen, Yundai Tian, Jie affil: Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China sug: subj: Carotid Arteries Staining and Labeling Microscopy Rats Male Animal Studies Comparative Studies Multicenter Studies Evaluation Research Validation Studies Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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