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    This study introduces a novel semi-supervised learning framework for 3D medical image segmentation, improving accuracy and generalization with enhanced pseudo-label reliability and structural modeling for reduced annotation needs.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Semi-supervised learning (SSL) shows promise for 3D medical image segmentation, but faces challenges with unstable training and poor generalization due to unreliable pseudo-labels and inadequate structural modeling in unlabeled data.
    • These issues are exacerbated in volumetric data by inter-class imbalance and complex spatial dependencies.

    Purpose of the Study:

    • To propose a novel semi-supervised framework for 3D medical image segmentation that enhances feature learning, consistency regularization, and pseudo-label reliability.
    • To address limitations of existing SSL methods in handling complex anatomical structures and limited annotations.

    Main Methods:

    • Developed a unified framework integrating a Confidence-aware Multi-level Fusion Network (CMFN) for multi-scale feature representation.
    • Introduced a Semantic-Enhanced Center Alignment (SECA) module to align group-level anatomical structures and mitigate pseudo-label semantic drift.
    • Implemented a Group-Guided Reliability Assessment (GGRA) module to improve pseudo-label reliability by modeling confidence errors within a group-aware structural context.

    Main Results:

    • The proposed framework demonstrated superior accuracy and generalization on abdominal organ segmentation in CT scans.
    • Effectiveness was validated across diverse anatomical structures, including left atrium and brain tumor segmentation in MRI scans (LA, BTCV, BraTS19 benchmarks).
    • Consistently outperformed state-of-the-art methods in limited annotation scenarios.

    Conclusions:

    • The integrated framework effectively enhances feature discriminability and pseudo-label reliability for 3D medical image segmentation.
    • The method offers a robust solution for segmentation tasks with limited annotations, showing strong performance across various medical imaging modalities and anatomical targets.