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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

Yuhuan Lu, Kenli Li, Bin Pu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 15, 2022
    PubMed
    Summary

    This study introduces IS-YOLOX, a novel deep learning model for segmenting fetal heart structures in ultrasound images. It automates a complex task, improving congenital heart disease diagnosis.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiology

    Background:

    • Prenatal echocardiography is crucial for diagnosing congenital heart disease (CHD).
    • Manual segmentation of fetal heart structures is time-consuming and requires expertise.
    • Accurate segmentation is vital for fetal growth assessment and CHD diagnosis.

    Purpose of the Study:

    • To develop an automated deep instance segmentation model for fetal cardiac anatomical structures.
    • To improve the efficiency and accuracy of structure detection in fetal ultrasound images.
    • To address the limitations of manual segmentation in prenatal echocardiography.

    Main Methods:

    • Proposed IS-YOLOX, a YOLOX-based deep instance segmentation neural network.
    • Implemented a new instance segmentation branch within a multi-task deep learning framework.
    • Introduced a novel three-level multi-level non-maximum suppression (NMS) mechanism for enhanced performance.

    Main Results:

    • The IS-YOLOX model achieved superior performance compared to nine baseline methods.
    • Demonstrated accurate instance segmentation of 13 anatomical structures in the fetal four-chamber view.
    • Outperformed existing approaches on clinical datasets for fetal ultrasound image analysis.

    Conclusions:

    • IS-YOLOX offers an efficient and accurate automated solution for fetal cardiac structure segmentation.
    • This method advances the diagnosis of congenital heart disease through improved image analysis.
    • Represents a significant step forward in applying deep learning to prenatal echocardiography.