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1. Deep learning-based transformation of H&E stained tissue into special stains

   https://doi.org/10.1364/CLEO_AT.2022.ATh2I.4  

   de Haan, Kevin; Zhang, Yijie; Zuckerman, Jonathan E.; Liu, Tairan; Rivenson, Yair; Wallace, W. Dean; Ozcan, Aydogan (May 2022, Optica Conference on Lasers and Electro-Optics (CLEO))

   We present a deep learning-based technique to computationally transform H&E-stained tissue sections into different special stains. We also demonstrate that this stain-to-stain transformation framework improves diagnostic accuracy over the use of H&E only. 

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2. Virtual histological stain transformations through cascaded deep neural networks

   Xilin Yang, Bijie Bai (October 2022, Optica Frontiers in Optics + Laser Science 2022 (FIO, LS))

   We present a deep learning-based framework to virtually transfer brightfield images of H&E-stained tissue slides to other types of stains using cascaded networks, providing high-quality images of special stains from existing H&E stained tissue images. 

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3. Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

   https://doi.org/10.1038/s41598-022-13332-9  

   Soltani, Soheil; Ojaghi, Ashkan; Qiao, Hui; Kaza, Nischita; Li, Xinyang; Dai, Qionghai; Osunkoya, Adeboye O.; Robles, Francisco E. (December 2022, Scientific Reports)

   Abstract Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. 

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4. 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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5. Biopsy-free virtual histology to discriminate benign from malignant squamous neoplasms

   Kokikian, N; Wahhab, R; Li, J; Martin, S; Beynet, D; Ozcan, A; Scumpia, P (April 2024, American Association for Cancer Research (AACR) Annual Meeting)

   Reflectance confocal microscopy (RCM) is a noninvasive optical imaging modality that allows for cellular-level resolution, in vivo images of skin without performing a traditional skin biopsy. RCM image interpretation currently requires specialized training to interpret the grayscale output images that are difficult to correlate with tissue pathology. Here, we use a deep learning-based framework that uses a convolutional neural network to transform grayscale output images into virtually-stained hematoxylin and eosin (H&E)-like images allowing for the visualization of various skin layers, including the epidermis, dermal-epidermal junction, and superficial dermis layers. To train the deep-learning framework, a stack of a minimum of 7 time-lapsed, successive RCM images of excised tissue were obtained from epidermis to dermis 1.52 microns apart to a depth of 60.96 microns using the Vivascope 3000. The tissue was embedded in agarose tissue and a curette was used to create a tunnel through which drops of 50% acetic acid was used to stain cell nuclei. These acetic acid-stained images were used as “ground truth” to train a deep convolutional neural network using a conditional generative adversarial network (GAN)-based machine learning algorithm to digitally convert the images into GAN-based H&E-stained digital images. We used the already trained machine learning algorithm and retrained the algorithm with new samples to include squamous neoplasms. Through further training and refinement of the algorithm, high-resolution, histological quality images can be obtained to aid in earlier diagnosis and treatment of cutaneous neoplasms. The overall goal of obtaining biopsy-free virtual histology images with this technology can be used to provide real-time outputs of virtually-stained H&E skin lesions, thus decreasing the need for invasive diagnostic procedures and enabling greater uptake of the technology by the medical community. 

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