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    Domain shift is a challenge in medical image segmentation. The Domain-Generalized Discrete Diffusion Model for Segmentation (DG-DDM-Seg) improves segmentation performance across different domains by extracting robust features and using pseudo-labels.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Domain shift significantly degrades medical image segmentation model performance on unseen data.
    • Variations in acquisition protocols and modalities cause domain shift, limiting model generalizability.

    Purpose of the Study:

    • To develop a novel diffusion-based generative model for single-source domain generalization in medical image segmentation.
    • To enhance the robustness and cross-domain performance of segmentation models.

    Main Methods:

    • Introduced the Domain-Generalized Discrete Diffusion Model for Segmentation (DG-DDM-Seg).
    • Employed robust feature extraction from conditional images to ensure domain independence.
    • Utilized a two-path reverse diffusion process with robust features and pseudo-labels for training.

    Main Results:

    • DG-DDM-Seg achieved state-of-the-art performance in cross-domain medical image segmentation.
    • Demonstrated effectiveness across domain shifts in modality, sequence, and site.
    • The model generates discrete conditional distributions of segmentation masks for unseen domains.

    Conclusions:

    • DG-DDM-Seg effectively addresses the domain shift problem in medical image segmentation.
    • The proposed methods enhance domain independence and cross-domain segmentation accuracy.
    • The diffusion-based approach offers a promising direction for generalized medical image analysis.