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Style Consistency Unsupervised Domain Adaptation Medical Image Segmentation.

Lang Chen, Yun Bian, Jianbin Zeng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 5, 2024
    PubMed
    Summary

    This study introduces a style consistency method for unsupervised domain adaptation in medical image segmentation. The approach effectively reduces domain shift between imaging modalities, improving segmentation accuracy for unlabeled target images.

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    Area of Science:

    • Medical image analysis
    • Computer vision
    • Machine learning

    Background:

    • Unsupervised domain adaptation (UDA) medical image segmentation aims to segment unlabeled target images using labeled source data.
    • Significant domain shift exists between different medical imaging modalities, hindering model generalization.
    • Existing UDA methods struggle with cross-modality segmentation due to variations in image characteristics.

    Purpose of the Study:

    • To propose a novel style consistency unsupervised domain adaptation method for medical image segmentation.
    • To mitigate the domain shift problem caused by different medical imaging modalities.
    • To enhance the segmentation performance on unlabeled target domain images.

    Main Methods:

    • A local phase-enhanced style fusion method was designed to reduce domain shift and enhance organs of interest.
    • A phase consistency discriminator was developed to disentangle domain-invariant and style-specific features.
    • Style consistency estimation and style consistency entropy were introduced to address difficult regions and improve organ integrity.

    Main Results:

    • The proposed method demonstrated superior performance in segmenting medical images across different modalities.
    • Experimental results on in-house and public datasets confirmed the framework's effectiveness.
    • The approach successfully improved the integrity and accuracy of segmentation for interested organs.

    Conclusions:

    • The style consistency method effectively addresses the domain shift challenge in unsupervised domain adaptation for medical image segmentation.
    • This framework offers a promising solution for cross-modality medical image segmentation tasks.
    • The proposed techniques enhance model robustness and segmentation quality in diverse imaging scenarios.