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1. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

   https://doi.org/10.1038/s41377-020-0315-y  

   Zhang, Yijie; de Haan, Kevin; Rivenson, Yair; Li, Jingxi; Delis, Apostolos; Ozcan, Aydogan (May 2020, Light: Science & Applications)

   Abstract Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain-specific deep neural networks. Here, we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images, in which different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information as its input: (1) autofluorescence images of the label-free tissue sample and (2) a “digital staining matrix”, which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H&E), Jones’ silver stain, and Masson’s trichrome stain. Using a single network, this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section, which is currently not feasible with standard histochemical staining methods. 

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2. Virtual staining of label-free tissue using deep learning

   https://doi.org/10.1117/12.2670987  

   Ozcan, Aydogan (August 2023, SPIE Optical Metrology)

   Ferraro, Pietro; Grilli, Simonetta; Psaltis, Demetri (Ed.)

   Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications. 

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3. Deep learning-enabled virtual histological staining of biological samples

   https://doi.org/10.1038/s41377-023-01104-7  

   Bai, Bijie; Yang, Xilin; Li, Yuzhu; Zhang, Yijie; Pillar, Nir; Ozcan, Aydogan (March 2023, Light: Science & Applications)

   Abstract Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to asvirtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications. 

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4. Deep learning-based transformation of H&E stained tissues into special stains

   https://doi.org/10.1038/s41467-021-25221-2  

   de Haan, Kevin; Zhang, Yijie; Zuckerman, Jonathan E.; Liu, Tairan; Sisk, Anthony E.; Diaz, Miguel F. P.; Jen, Kuang-Yu; Nobori, Alexander; Liou, Sofia; Zhang, Sarah; et al (August 2021, Nature Communications)

   Abstract Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost. 

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5. Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics

   https://doi.org/10.34133/2022/9847962  

   Soltani, Soheil; Cheng, Brian; Osunkoya, Adeboye O.; Robles, Francisco E. (January 2022, BME Frontiers)

   Objective and Impact Statement . Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge. In this work, we developed an approach based on label-free, high-resolution molecular imaging with multispectral deep ultraviolet (UV) microscopy which identifies important prostate tissue components, including basal cells. This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer. Introduction . One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts. However, identifying basal cells using hematoxylin and eosin (H&E) stains, which is the standard of care, can be difficult in a subset of cases. In such situations, pathologists often resort to immunohistochemical (IHC) stains for a definitive diagnosis. However, IHC is expensive and time-consuming and requires more tissue sections which may not be available. In addition, IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis. Methods . We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells, luminal cells, and inflammatory cells. The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information. Results . Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity, free of any staining procedures, based on the presence or absence of basal cells. We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells, even in cases where IHC stains fail. Conclusion . Our simple, low-cost, and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components. 

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