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A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm.

Shijia Zhou, Pradeeba Sridar, Narelle June Kennedy

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    PubMed
    Summary
    This summary is machine-generated.

    Accurately measuring fetal thalamus diameter (FTD) is crucial for understanding maternal factors affecting fetal brain development. A new deep learning algorithm, FTDNet, automates this measurement, outperforming traditional methods.

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    Area of Science:

    • Neuroimaging
    • Developmental Biology
    • Medical Artificial Intelligence

    Background:

    • Maternal factors influencing fetal thalamus development require accurate fetal thalamus diameter (FTD) measurement.
    • Routine 2D ultrasound (2D-US) does not measure FTD, and manual measurement is difficult due to the thalamus's indistinct boundaries in fetal brain images.
    • Existing statistical shape model (SSM) methods struggle with FTD measurement accuracy because of image noise and fuzzy edges.

    Purpose of the Study:

    • To develop and validate a deep learning-based algorithm, FTDNet, for the automatic measurement of FTD from 2D-US images.
    • To overcome the limitations of manual and traditional SSM-based FTD measurement methods.

    Main Methods:

    • A novel deep learning algorithm, FTDNet, was developed for end-to-end thalamus detection and FTD landmark identification using supervised learning.
    • A curated dataset of 1,111 fetal thalamus images with expert-annotated landmark coordinates and bounding boxes was utilized.
    • Intraclass correlation coefficient (ICC) was employed to evaluate FTDNet's measurement consistency against ground truth.

    Main Results:

    • FTDNet achieved a significant intraclass correlation coefficient (ICC) score of 0.734 in measuring FTD.
    • The proposed FTDNet algorithm demonstrated superior performance compared to traditional SSM methods and other baseline approaches.
    • The algorithm successfully detects the thalamus region and focuses on landmark detection for accurate FTD measurement.

    Conclusions:

    • FTDNet provides an accurate and automated method for measuring FTD, significantly advancing research on maternal influences on fetal brain development.
    • This deep learning approach offers a potential breakthrough for studying fetal thalamus development and its relationship with maternal factors.
    • The developed algorithm has clinical relevance for improving the assessment of fetal brain development through ultrasound imaging.