Abstract
PurposeHistological 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.ProceduresIn 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.ResultsThe 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.ConclusionsThis 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.
Highlights
Coronary artery disease (CAD) is the leading cause of mortality globally
We propose a deep learning-based virtual staining method to generate virtually stained images from bright-field microscopic images of unlabeled rat carotid artery tissue sections imaged with a conventional wide-field microscope (Fig. 1)
These generated images demonstrated that the conditional generative adversarial networks (cGANs) can transform brightfield images of unstained tissue sections (Figs. 3a–g) into the corresponding colorized images that are expected from hematoxylin and eosin (H&E), picrosirius red (PSR), and orcein-stained tissue sections
Summary
Coronary artery disease (CAD) is the leading cause of mortality globally. Fundamental studies regarding CADLi D. et al.: Deep Learning for Virtual Histological Images pathophysiological mechanisms and potential therapeutic methods have considerable clinical and scientific significance, which highly rely on histology analysis of artery tissue sections [1,2,3,4]. The variability of histologically stained tissue sections in these irreversible steps introduces major challenges in histopathological image analysis. These variations are due to human-to-human variability, differences in protocols and microscopes between labs, and color variations in staining procedures [5]. The time-consuming histological staining procedures create obstacles for fast pathological diagnosis. Tissuesectioning microscopies, such as confocal and multiphoton microscopes, have been applied for non-invasive volumetric or quantitative measurements of artery tissue sections to accelerate and improve the microscopic imaging step in this workflow [6, 7]. Tissue-sectioning microscopy requires fluorescence agents as imaging probes in contrast to specific artery tissue compositions
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