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Automated 3D Perioral Landmark Detection Using High-Resolution Network: Artificial Intelligence-based Anthropometric

Yuyan Yang, Mengyuan Zhang, Yicheng An

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    Summary

    This study introduces an automated 3D facial landmarking method using deep learning for faster, more accurate perioral anthropometric analysis in plastic surgery. The novel approach significantly reduces manual effort and error in 3D facial modeling.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Plastic Surgery

    Background:

    • Three-dimensional facial stereophotogrammetry is valuable in plastic surgery for planning and efficacy evaluation.
    • Manual landmark identification is time-consuming and prone to error.
    • Automated 3D facial landmark localization offers efficiency and accuracy improvements.

    Purpose of the Study:

    • To present a novel deep-learning method for automated 3D perioral landmark annotation.
    • To enable efficient and precise anthropometric data acquisition from 3D facial models.

    Main Methods:

    • A 3D facial model is transformed into 2D images for key-point detection using a High-Resolution Network.
    • 2D key-point coordinates are mapped back to the 3D model to determine 3D landmark coordinates.
    • The method was trained on 120 models and validated on 50 models.

    Main Results:

    • The automated method achieved a mean accuracy of 1.30 mm for landmark detection.
    • Processing time averaged 5.2 seconds per model.
    • Subsequent measurements showed mean errors of 0.87 mm for linear and 5.62° for angular analyses.

    Conclusions:

    • The developed automated 3D perioral landmarking method is effective for plastic surgery.
    • It facilitates fast and accurate anthropometric analysis of lip morphology.
    • This tool supports aesthetic procedures requiring precise facial measurements.