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Background Matters: A Cross-View Bidirectional Modeling Framework for Semi-Supervised Medical Image Segmentation.

Luyang Cao, Jianwei Li, Yinghuan Shi

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 4, 2025
    PubMed
    Summary

    This study introduces Cross-view Bidirectional Modeling (CVBM) for semi-supervised medical image segmentation. By incorporating background modeling, CVBM enhances foreground segmentation accuracy, even outperforming fully supervised methods with limited labeled data.

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    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Machine Learning

    Background:

    • Semi-supervised medical image segmentation (SSMIS) reduces the need for manual annotations by utilizing unlabeled data.
    • Current SSMIS methods primarily focus on foreground segmentation, neglecting the potential benefits of explicit background modeling.

    Purpose of the Study:

    • To demonstrate the benefits of background modeling in enhancing foreground segmentation confidence.
    • To propose and validate the Cross-view Bidirectional Modeling (CVBM) framework for improved SSMIS.

    Main Methods:

    • Developed the Cross-view Bidirectional Modeling (CVBM) framework incorporating background modeling as an auxiliary task.
    • Implemented a bidirectional consistency mechanism to align foreground and background-guided predictions.
    • Evaluated the framework on LA, Pancreas, ACDC, and HRF datasets.

    Main Results:

    • CVBM achieves state-of-the-art performance across multiple medical image segmentation datasets.
    • On the Pancreas dataset, CVBM surpassed fully supervised methods using only 20% of labeled data (DSC: 84.57% vs. 83.89%).

    Conclusions:

    • Explicitly modeling the background region significantly enhances foreground segmentation in SSMIS.
    • CVBM offers a novel and effective approach for semi-supervised medical image segmentation, reducing data annotation requirements.