Virtual staining creates H&E-like images with minimal tissue processing. Typically, two channels are used, but single-channel staining is attractive for techniques like reflectance confocal microscopy (RCM). Our study trains a deep learning model to generate H&E images from single-channel RCM using pixel-level registration. Porcine skin was stained with acridine orange, SR101, and aluminum chloride, and confocal microscopy images were acquired. Using pix2pixGAN, we trained the model on grayscale RCM images, producing virtual stained images that closely resembled the ground truth. We showed some model output examples and used image assessment metrics to evaluate model performance. This technique has potential for in vivo surgical applications, eliminating the need for image registration.
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