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Gaze-Guided Medical Image Segmentation: A Training-Free Approach using SAM Foundation Model.

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    Summary
    This summary is machine-generated.

    Eye-gaze data offers an efficient, training-free method for medical image segmentation using foundation models. This approach rivals trained models and surpasses manual bounding boxes, enhancing accessibility in clinical settings.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Medical image segmentation is crucial but labor-intensive, necessitating automated solutions.
    • Deep learning models require extensive labeled data and training, limiting accessibility.
    • Foundation models like Segment Anything Model (SAM) offer zero-shot segmentation but often need task-specific adapters.
    • Training-free approaches are essential for resource-limited clinical centers.

    Purpose of the Study:

    • To investigate eye-gaze data as an implicit, efficient prompt for training-free SAM-based medical image segmentation.
    • To evaluate gaze-based prompting strategies as a low-cost alternative to manual bounding boxes.
    • To demonstrate the clinical relevance and accessibility of gaze-driven segmentation.

    Main Methods:

    • Utilized eye-gaze data as implicit prompts for the Segment Anything Model (SAM).
    • Evaluated multiple gaze-based prompting strategies, including combining bounding boxes with gaze-derived heatmaps.
    • Validated the approach on polyp segmentation (Kvasir-SEG) and prostate segmentation (NCI-ISBI 2013).

    Main Results:

    • Gaze-based prompting achieved satisfactory segmentation results comparable to SAM-based trained models.
    • The proposed gaze-based method outperformed segmentation using only manual bounding boxes.
    • The most effective strategy involved a combination of bounding boxes and gaze data heatmaps.

    Conclusions:

    • Eye-gaze data provides a natural, efficient, and low-cost prompting mechanism for foundation models in medical imaging.
    • This training-free, gaze-driven approach enhances segmentation automation, reducing annotation time and enabling near real-time applications.
    • The method improves accessibility for resource-limited clinical settings, facilitating faster deployment and broader adaptability.