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Updated: Apr 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Divergence of Gradient Convolution: Deformable Segmentation With Arbitrary Initializations.

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    Summary
    This summary is machine-generated.

    We introduce a novel unified approach for deformable model-based segmentation using a gradient convolution field (GCF). This method enhances contour evolution, handles arbitrary initializations, and improves performance against noise and variations.

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    Area of Science:

    • Medical Image Analysis
    • Computer Vision

    Background:

    • Deformable models are crucial for image segmentation.
    • Existing methods face challenges with initialization, noise, and variations.

    Purpose of the Study:

    • To propose a unified, robust deformable model-based segmentation approach.
    • To enhance contour evolution using a novel image force field.

    Main Methods:

    • Computing divergence of a gradient convolution field (GCF) for image force.
    • Deriving a salient representation for contour evolution.
    • Utilizing convex relaxation and gradient descent with intrinsic regularization.

    Main Results:

    • The method achieves global minimum and handles arbitrary initializations.
    • The force field combines edge-based and region-based properties.
    • Nonlinear diffusion improves noise handling; 2D to 3D extension demonstrated.

    Conclusions:

    • The proposed method offers greater flexibility and superior performance over state-of-the-art techniques.
    • It shows enhanced robustness to noise and appearance variations.
    • The unified approach advances deformable segmentation capabilities.