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    Area of Science:

    • Digital pathology
    • Computational pathology
    • Artificial intelligence in medicine

    Background:

    • Cancer diagnosis relies on hematoxylin and eosin (HE) stained pathological slides from formalin-fixed, paraffin-embedded (FFPE) tissues.
    • Traditional HE staining is time-consuming and resource-intensive.
    • Virtual staining using generative models is a promising alternative but faces challenges with FFPE image quality.

    Purpose of the Study:

    • To develop an advanced virtual staining method for high-quality FFPE-to-HE image generation.
    • To address the challenge of blurred structures in FFPE images for accurate virtual staining.
    • To introduce a novel Multiple Cell Semantics-guided supervised generative adversarial model (MCS-Stain).

    Main Methods:

    • Developed MCS-Stain, a generative adversarial model incorporating multiple cell semantic guidance.
    • Utilized pretrained cell semantic guidance (PCSM) to align image features.
    • Incorporated cell mask guidance and dynamic cell semantic guidance during training.
    • Evaluated performance on FFPE-to-HE datasets against state-of-the-art methods.

    Main Results:

    • MCS-Stain significantly outperformed existing methods in qualitative and quantitative FFPE-to-HE virtual staining.
    • Results demonstrated robustness across different pretrained cell segmentation models (PCSMs) and data sources.
    • Dynamic cell semantic guidance showed potential for other virtual staining tasks, like HE to immunohistochemical (IHC) staining.

    Conclusions:

    • MCS-Stain offers a significant advancement in virtual staining technology for digital pathology.
    • The model provides a more efficient and effective alternative to traditional HE staining.
    • Further applications of dynamic cell semantic guidance in virtual staining are promising.