Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks

Xu Lei; Fahad Parvez Mahdi; Ze Jin; Hao Sun; Yoshiyuki Noguchi; Masayuki Murata; Kenji Suzuki

doi:10.1117/12.2652265

6 April 2023 Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks

Xu Lei, Fahad Parvez Mahdi, Ze Jin, Hao Sun, Yoshiyuki Noguchi, Masayuki Murata, Kenji Suzuki

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710P (2023) https://doi.org/10.1117/12.2652265
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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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Citation Download Citation

Xu Lei, Fahad Parvez Mahdi, Ze Jin, Hao Sun, Yoshiyuki Noguchi, Masayuki Murata, and Kenji Suzuki "Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710P (6 April 2023); https://doi.org/10.1117/12.2652265

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