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Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.

Caizi Li, Li Dong, Qi Dou

    IEEE Journal of Biomedical and Health Informatics
    |August 10, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel self-ensembled co-training framework for segmenting COVID-19 lesions in CT scans using limited labeled data. The method effectively utilizes unlabeled data, improving diagnostic accuracy for coronavirus disease 2019.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Coronavirus disease 2019 (COVID-19) presents a significant global health challenge.
    • Accurate segmentation of COVID-19 lesions in computed tomography (CT) scans is crucial for disease monitoring and treatment.
    • Manual annotation of CT scans is time-consuming and labor-intensive, necessitating automated methods.

    Purpose of the Study:

    • To develop an automated method for segmenting COVID-19 lesions from CT scans.
    • To address the challenge of limited labeled data in medical image segmentation.
    • To improve the efficiency and accuracy of COVID-19 diagnosis support systems.

    Main Methods:

    • A self-ensembled co-training framework was proposed, utilizing both limited labeled and large-scale unlabeled CT scan data.
    • Two collaborative models were employed, teaching each other through pseudo-labeling of unlabeled data to enhance diversity.
    • A self-ensembling strategy with moving average was implemented for consistency regularization to mitigate noisy pseudo-labels.

    Main Results:

    • The proposed framework demonstrated superior performance in segmenting COVID-19 lesions compared to state-of-the-art semi-supervised methods.
    • Effective segmentation was achieved even with a minimal amount of labeled data (4 CT scans).
    • The method showed significant potential for automated analysis of COVID-19 in CT imaging.

    Conclusions:

    • The self-ensembled co-training framework offers a promising solution for automated COVID-19 lesion segmentation with limited labeled data.
    • This approach can significantly reduce the burden of manual annotation in clinical practice.
    • The study highlights the effectiveness of semi-supervised learning in medical image analysis for emerging infectious diseases.