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Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

Xiaomeng Li, Lequan Yu, Hao Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |June 2, 2020
    PubMed
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    This study introduces a novel semisupervised deep learning method for medical image segmentation, effectively utilizing unlabeled data to improve accuracy in tasks like skin lesion and liver segmentation.

    Area of Science:

    • Medical Imaging
    • Deep Learning
    • Computer Vision

    Background:

    • Supervised deep learning for medical image segmentation is limited by the scarcity and high cost of labeled data.
    • Acquiring large, annotated datasets for medical imaging tasks is a significant bottleneck in model development.

    Purpose of the Study:

    • To develop a semisupervised method for medical image segmentation that leverages both labeled and unlabeled data.
    • To enhance the performance of medical image segmentation models by addressing the challenge of limited labeled data.

    Main Methods:

    • A semisupervised approach optimizing a weighted combination of supervised and regularization losses.
    • Incorporation of a transformation-consistent strategy within a self-ensembling model for enhanced regularization.

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  • Utilizing a teacher model (averaged student weights) to optimize consistency loss with generalized transformations.

    • Main Results:

    • The proposed method demonstrated superior performance on challenging 2-D and 3-D medical image segmentation tasks.
    • Achieved state-of-the-art results in skin lesion, optic disk, and liver segmentation.
    • Effectively utilized unlabeled data to improve segmentation accuracy.

    Conclusions:

    • The developed semisupervised method is highly effective for medical image segmentation, particularly when labeled data is scarce.
    • The approach offers a robust solution for improving segmentation accuracy across diverse medical imaging modalities.
    • This work advances the application of deep learning in medical image analysis by overcoming data limitations.