We employed a Pix2Pix generative adversarial network to translate the multispectral fluorescence images into colored brightfield representations resembling H&E staining. The model underwent training using 512x512 pixel paired image patches, with a manually stained image serving as the reference and the fluorescence images serving as the processing input. The baseline model, without any modifications, did not achieve high microscopic accuracy, manifesting incorrect color attribution to various biological structures and the addition or removal of image features. However, through the substitution of simple convolutions with Dense convolution units in the U-Net Generator, we observed an increase in the similarity of microscopic structures and the color balance between the paired images. The resulting improvements underscore the potential utility of virtual staining in histopathological analysis for veterinary oncology applications.
Skin and subcutaneous tumors are common in companion animals, that can be difficult to diagnose and treat. Raman spectroscopy shows high diagnostic accuracy in identifying malignant tumors and benign lipomas in dogs and cats. However, the traditional single-point raster scanning approach is not ideal for a large-field-of-view Raman imaging due to its time-consuming nature when scanning areas larger than a square centimeter. Additionally, focusing the excitation spot can lead to high levels of light fluency (J/cm2), potentially causing damage to tissue biomolecules. Furthermore, the resulting raster-scan image often lacks sufficient spatial resolution to effectively compare it with tissue morphology findings. In this study, we focused on implementing EMCCD camera-based Raman imaging to accurately capture Raman spectral band signatures and overcome autofluorescence interference in veterinary cancer samples ex vivo. By utilizing the tunable band-pass filters set-up, our system enables large-field-of-view imaging of specific Raman bands, such as the 1437 cm-1 band or 1652 cm-1 band in biological tissue, proposing a more efficient, accurate and safe approach for Raman imaging in the veterinary field.
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