Digital staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, digital staining offers an efficient and low-infrastructure alternative. Researchers can expedite tissue analysis without physical sectioning by leveraging microscopy-based techniques, such as confocal microscopy. However, interpreting grayscale or pseudo-color microscopic images remains challenging for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, designed explicitly for digital staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. Using aluminum chloride preprocessing for skin tissue, we enhance nuclei contrast in RCM images. We trained the model with digital H&E labels featuring two fluorescence channels, eliminating the need for image registration and providing pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for digital staining tasks and advancing the field of histological image analysis.
Keywords: Digital staining; Generative adversarial network (GAN); In-and-out Net; Reflectance confocal microscopy (RCM).
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