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Related Experiment Video

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Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation.

Shumeng Li, Ziyuan Zhao, Kaixin Xu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study presents a new semi-supervised method for segmenting 3D left atrium MR images, reducing the need for costly annotations. The hierarchical consistency regularized mean teacher framework achieves competitive performance, matching fully supervised approaches.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiovascular Imaging

    Background:

    • Deep learning shows potential for 3D left atrium segmentation in MR images.
    • Manual image annotation is resource-intensive and challenging.

    Purpose of the Study:

    • To develop a novel semi-supervised framework for 3D left atrium segmentation.
    • To reduce reliance on extensive manual annotations.

    Main Methods:

    • Introduced a hierarchical consistency regularized mean teacher framework.
    • Employed multi-scale deep supervision and hierarchical consistency regularization for student model optimization.

    Main Results:

    • Achieved competitive segmentation performance comparable to fully supervised methods.
    • Outperformed existing state-of-the-art semi-supervised segmentation techniques.

    Conclusions:

    • The proposed method effectively addresses the challenge of limited annotations in 3D left atrium segmentation.
    • This framework offers a promising alternative for efficient and accurate cardiac image analysis.