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ProGIS: Prototype-Guided Interactive Segmentation for Pathological Images.

Jiusong Ge, Di Zhang, Yingkang Zhan

    IEEE Transactions on Medical Imaging
    |September 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Prototype-Guided Interactive Segmentation (ProGIS) enhances pathological image analysis by efficiently segmenting tissues with minimal user input. This new framework achieves state-of-the-art results for both region and slide-level segmentation tasks.

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    Area of Science:

    • Computational pathology
    • Medical image analysis
    • Digital pathology

    Background:

    • Traditional automatic segmentation in pathology struggles with accuracy and requires extensive data.
    • Current interactive methods face challenges in balancing interaction cost and performance, especially for slide-level analysis.

    Purpose of the Study:

    • To introduce a novel Prototype-Guided Interactive Segmentation (ProGIS) framework for efficient and precise pathological image segmentation.
    • To improve upon existing interactive segmentation methods by reducing interaction costs and enhancing adaptability to slide-level tasks.

    Main Methods:

    • ProGIS utilizes a three-module approach: Prototype Initialization, Prototype Navigation, and Local Refinement.
    • It identifies tissue components via categorical prototypes and refines segmentation with detailed correction signals.
    • The framework supports multi-class segmentation without predefined categories and adapts to slide-level analysis.

    Main Results:

    • ProGIS achieved state-of-the-art performance on both region of interest and slide-level pathological segmentation datasets.
    • The framework demonstrated superior efficiency, requiring fewer interactions compared to existing methods.
    • Successful adaptation for slide-level interactive segmentation was confirmed.

    Conclusions:

    • ProGIS offers a significant advancement in interactive pathological image segmentation, providing precise results with minimal interaction.
    • The framework's adaptability and efficiency make it highly suitable for routine pathology analysis.
    • The developed method sets a new benchmark for interactive segmentation in computational pathology.