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Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation.

Chongwen Lyu, Kai Han, Lu Liu

    IEEE Journal of Biomedical and Health Informatics
    |June 30, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a semi-supervised method for fetal ultrasound image segmentation, improving accuracy with bidirectional consistency and reducing the need for extensive labeled data in medical AI.

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    Area of Science:

    • Medical Imaging Analysis
    • Artificial Intelligence in Healthcare
    • Fetal Medicine

    Background:

    • Accurate fetal ultrasound (US) image segmentation is crucial for prenatal care and surgical planning.
    • Deep learning for fetal US segmentation is hindered by the scarcity of large, annotated datasets.
    • Current methods struggle with the time and labor required for data annotation.

    Purpose of the Study:

    • To develop an efficient semi-supervised method for fetal US image segmentation.
    • To overcome the limitations of data annotation in deep learning for medical imaging.
    • To improve the accuracy and applicability of AI in fetal development assessment.

    Main Methods:

    • Proposed a novel semi-supervised method named bidirectional prototype-guided consistency constraint (BiPCC).
    • Utilized prototypes to bridge labeled and unlabeled data, enabling interaction and consistency.
    • Incorporated uncertainty-based cross-supervision to enhance pseudo-label quality.

    Main Results:

    • BiPCC significantly outperformed existing state-of-the-art methods in semi-supervised fetal US segmentation.
    • Demonstrated strong generalization capabilities across diverse medical image segmentation tasks.
    • Validated performance on two distinct fetal US datasets and two additional medical imaging datasets.

    Conclusions:

    • The BiPCC method offers a novel approach to semi-supervised fetal US image segmentation.
    • The technique effectively leverages limited labeled data to improve segmentation accuracy.
    • This advancement holds significant potential for intelligent healthcare and prenatal diagnostics.