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Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning.

Zahra Sobhaninia, Shima Rafiei, Ali Emami

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces a deep learning model for automatically measuring fetal head circumference (HC) from ultrasound images. The AI accurately segments fetal heads and estimates HC, aiding prenatal assessments.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Obstetrics

    Background:

    • Ultrasound imaging is crucial for prenatal diagnosis and estimating gestational age.
    • Fetal head circumference (HC) is a key biometric for assessing fetal growth and health.
    • Accurate HC measurement is vital for monitoring fetal development.

    Purpose of the Study:

    • To develop a multi-task deep convolutional neural network for automatic fetal head segmentation and HC ellipse estimation.
    • To improve the accuracy and efficiency of HC measurements in prenatal ultrasound.
    • To provide a reliable tool for assessing fetal growth and health.

    Main Methods:

    • A multi-task deep convolutional neural network was designed.
    • The network was trained to perform simultaneous segmentation of the fetal head and estimation of its elliptical parameters.
    • A compound cost function combining segmentation dice score and Mean Squared Error (MSE) of ellipse parameters was minimized.

    Main Results:

    • Experimental results demonstrated high accuracy in fetal head segmentation and HC ellipse estimation on a diverse ultrasound dataset.
    • The model's segmentation and HC evaluation performance closely matched radiologist annotations.
    • Achieved dice scores for segmentation and accuracy for HC evaluation were comparable to state-of-the-art methods.

    Conclusions:

    • The proposed multi-task deep learning approach effectively automates fetal head segmentation and HC estimation from ultrasound images.
    • This AI-driven method shows significant potential for enhancing prenatal diagnostic accuracy and efficiency.
    • The findings support the integration of advanced AI tools in routine obstetric ultrasound examinations.