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Author: Popp, Juergen × Publisher: Weinheim : Wiley-VCH-Verl. ×

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    In vivo coherent anti-Stokes Raman scattering microscopy reveals vitamin A distribution in the liver
    (Weinheim : Wiley-VCH-Verl., 2021) Rodewald, Marko; Bae, Hyeonsoo; Huschke, Sophie; Meyer-Zedler, Tobias; Schmitt, Michael; Press, Adrian Tibor; Schubert, Stephanie; Bauer, Michael; Popp, Juergen
    Here we present a microscope setup for coherent anti-Stokes Raman scattering (CARS) imaging, devised to specifically address the challenges of in vivo experiments. We exemplify its capabilities by demonstrating how CARS microscopy can be used to identify vitamin A (VA) accumulations in the liver of a living mouse, marking the positions of hepatic stellate cells (HSCs). HSCs are the main source of extracellular matrix protein after hepatic injury and are therefore the main target of novel nanomedical strategies in the development of a treatment for liver fibrosis. Their role in the VA metabolism makes them an ideal target for a CARS-based approach as they store most of the body's VA, a class of compounds sharing a retinyl group as a structural motive, a moiety that is well known for its exceptionally high Raman cross section of the C=C stretching vibration of the conjugated backbone.
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    Deep learning a boon for biophotonics
    (Weinheim : Wiley-VCH-Verl., 2020) Pradhan, Pranita; Guo, Shuxia; Ryabchykov, Oleg; Popp, Juergen; Bocklitz, Thomas W.
    This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim