Generative AI Methods in Fluorescent Virtual Staining
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This thesis investigates the application of advanced deep learning architectures, specifically
the U-Net and the conditional Wasserstein GAN with gradient penalty (cWGAN-GP), in
predicting fluorescent staining from transmitted light microscopy images, using differential
interference contrast (DIC) imaging. Fluorescence staining is a technique in microscopy used
in biomedical and material sciences to gain detailed visualizations of cellular structures and
functions through the use of a sizeable variety of available fluorophores, while also allowing
simultaneous imaging of different cellular and subcellular components. However, while
traditional fluorescence microscopy is informative, it is also hindered by high costs,
phototoxicity, and labor-intensive preparations. This research addresses these limitations by
utilizing AI-driven models on a diverse dataset from the Light My Cells Grand Challenge,
which includes both transmitted light images and fluorescent targets. After a thorough review
of the deep learning theoretical fundamentals, this study examines both single-layer and
multi-layer (z-stack) input configurations to evaluate their performance in generating highquality predictions. Key challenges such as handling target image sparsity and the complexity
introduced by multi-channel data are discussed, alongside the trade-offs in preprocessing
techniques like resizing and tiling. The findings demonstrate the potential of AI in
histological practices, providing cost-effective, and non-invasive alternatives. Future
research directions include mixed modality datasets and improved normalization techniques.
Overall, this thesis aims to contribute to the research of AI applications in the field of
biomedical imaging with respect to in silico staining solutions.