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Updated: Aug 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Interactive Object Segmentation With Inside-Outside Guidance.

Shiyin Zhang, Shikui Wei, Jun Hao Liew

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    This summary is machine-generated.

    This study introduces Inside-Outside Guidance (IOG), a new method for precise object segmentation masks with minimal human clicks. IOG achieves state-of-the-art results across diverse datasets and domains.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Precise object segmentation is crucial for various computer vision tasks.
    • Minimizing human interaction cost in segmentation is a significant challenge.

    Purpose of the Study:

    • To develop an efficient and accurate object segmentation method with reduced human interaction.
    • To introduce the Inside-Outside Guidance (IOG) interaction scheme.

    Main Methods:

    • Propose the Inside-Outside Guidance (IOG) scheme using one foreground and four background clicks.
    • Leverage an inside point near the object center and two outside points within a bounding box.
    • Utilize clicks to remove background distractions and internal irrelevant regions.

    Main Results:

    • Achieve state-of-the-art performance on benchmarks like GrabCut, PASCAL, and MS COCO.
    • Demonstrate strong generalization across street scenes, aerial imagery, and medical images.
    • IOG effectively handles segmentation challenges and supports further corrections.

    Conclusions:

    • IOG offers a simple yet effective approach to precise object segmentation.
    • The method significantly reduces human interaction cost while maintaining high accuracy.
    • IOG shows broad applicability and robust performance across diverse image domains.