Fluorescence imaging is used to visualize biological processes at molecular and cellular level. However, it has several limitations e.g., potential toxicity of fluorescent dyes, photobleaching and sensitivity to the environment. On the contrary, differential interference contrast (DIC) microscopes provide pseudo-3D images by enhancing contrast in unstained specimens and are harmless, and non-invasive compared to fluorescence imaging. This study proposes a massive-training artificial neural network (MTANN) scheme to generate simulated fluorescence images from DIC images. Experimental results showed that the proposed method generated fluorescence images of the proteins (sum of troponin T and vimentin) with SSIM value 0.878 which is close to the corresponding ‘gold standard’ images. Thus, it can be said that the proposed method contributes to harmless cell imaging.
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