Virtual UV fluorescence microscopy from hematoxylin and eosin staining of liver images using deep learning convolutional neural network

Abstract

The use of UV (ultraviolet fluorescence) light in microscopy allows improving the quality of images and observation of structures that are not visible in visible spectrum. The disadvantage of this method is the degradation of microstructures in the slide due to exposure to UV light. The article examines the possibility of using a convolutional neural network to perform this type of conversion without damaging the slides. Using eosin hematoxylin stained slides, a database of image pairs was created for visible light (halogen lamp) and UV light. This database was used to train a multi–layer unidirectional convolutional neural network. The results of the study were subjectively and objectively assessed using the SSIM (Structural Similarity Index Measure) and SSIM (structure only) image quality measures. The results show that it is possible to perform this type of conversion (the studies used liver slides for 100× magnification), and in some cases there was an additional improvement in image quality.