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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiology

    Background:

    • Automating cardiac structure delineation from 2D echocardiograms is crucial for clinical diagnosis.
    • Deep convolutional neural networks (CNNs) show promise for image segmentation tasks.

    Purpose of the Study:

    • To evaluate the performance of state-of-the-art encoder-decoder deep CNNs for cardiac structure segmentation and clinical index estimation in 2D echocardiographic images.
    • To introduce and utilize the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMSUS) dataset.

    Main Methods:

    • Development and application of encoder-decoder deep CNN architectures.
    • Utilizing the CAMSUS dataset, the largest publicly available annotated dataset for echocardiographic assessment, comprising 500 patients' data.
    • Comparison with non-deep learning methods and expert cardiologist measurements.

    Main Results:

    • Encoder-decoder CNNs outperformed traditional non-deep learning methods.
    • Accurate reproduction of end-diastolic and end-systolic left ventricular volumes (mean correlation 0.95, absolute mean error 9.5 ml).
    • Left ventricular ejection fraction estimation showed good performance (mean correlation 0.80, absolute mean error 5.6%), slightly below intra-observer variability.

    Conclusions:

    • Deep learning methods demonstrate high potential for automated analysis of 2D echocardiographic images.
    • The CAMSUS dataset facilitates further research in this domain.
    • Future work can focus on improving performance to match or exceed inter-observer variability for fully automated cardiac assessments.