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Related Experiment Video

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3D Whole-heart Myocardial Tissue Analysis
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Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography.

Borge Solli Andreassen, Federico Veronesi, Olivier Gerard

    IEEE Journal of Biomedical and Health Informatics
    |December 14, 2019
    PubMed
    Summary

    A new fully automatic method accurately segments the mitral annulus in 3D Transesophageal Echocardiography (3D TEE) images. This innovation reduces manual effort and inter-observer variability in cardiac assessments.

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    Area of Science:

    • Cardiovascular Imaging
    • Medical Image Analysis
    • Artificial Intelligence in Medicine

    Background:

    • 3D Transesophageal Echocardiography (3D TEE) is crucial for mitral valve assessment and cardiac intervention guidance.
    • Accurate mitral annulus segmentation is essential for quantifying various cardiac parameters.

    Purpose of the Study:

    • To introduce a fully automatic method for mitral annulus segmentation in 3D TEE.
    • To eliminate the need for manual input and reduce inter-observer variability.

    Main Methods:

    • A deep 2D convolutional neural network was trained on 111 multi-frame 3D TEE recordings.
    • Exploited left ventricular symmetry to decompose 3D data into 2D planes.
    • Regularized predictions from neighboring planes for annulus continuity.

    Main Results:

    • Achieved a mean segmentation error of 2.0 mm with a standard deviation of 1.9 mm on the test set.
    • Demonstrated the feasibility of fully automatic mitral annulus segmentation.

    Conclusions:

    • Fully automatic mitral annulus segmentation in 3D TEE is feasible.
    • This method can streamline the quantification of mitral annular parameters.
    • Potential to significantly reduce inter-observer variability in cardiac imaging analysis.