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DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation.

Xinyu Yang, Yuan Zhang, Benny Lo

    IEEE Journal of Biomedical and Health Informatics
    |October 2, 2020
    PubMed
    Summary

    A new deep adversarial network, DBAN, accurately segments cardiac MRI, offering results comparable to clinical experts for improved heart function analysis.

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    Area of Science:

    • Medical Artificial Intelligence
    • Deep Learning in Medical Imaging
    • Cardiac MRI Analysis

    Background:

    • Accurate segmentation of cardiac structures in short-axis cardiac MRI is crucial for diagnosing heart conditions.
    • Existing methods often struggle with precise delineation of the left ventricle, right ventricle, and myocardium.
    • The advancement of deep neural networks offers potential for automated and accurate segmentation.

    Purpose of the Study:

    • To propose a novel deep adversarial network, the dilated block adversarial network (DBAN), for automated segmentation of cardiac MRI.
    • To enhance the capture and aggregation of multi-scale features for improved segmentation accuracy.
    • To evaluate the performance of DBAN against state-of-the-art methods and clinical expert standards.

    Main Methods:

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    • Development of DBAN, comprising a segmentor with dilated blocks (DB) and a discriminator.
    • DBs designed to capture and aggregate multi-scale features for enhanced segmentation.
    • Utilizing a discriminator to differentiate segmentation maps from ground truth and provide confidence maps to guide the segmentor.

    Main Results:

    • DBAN achieved state-of-the-art performance on the Accuracy, Consistency, and Delineation Challenge (ACDC) dataset.
    • Quantitative analysis showed cardiac function indices derived from DBAN were similar to those from clinical experts.
    • The model demonstrated robust segmentation of left ventricle, right ventricle, and myocardium.

    Conclusions:

    • DBAN represents a significant advancement in automated cardiac MRI segmentation.
    • The proposed method shows potential for clinical application in short-axis cardiac MRI analysis.
    • DBAN offers a reliable tool for assessing cardiac function with accuracy comparable to human experts.