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使用高分辨率网络进行自动化3D外围地标检测:基于人工智能的人类测量分析.

Yuyan Yang, Mengyuan Zhang, Yicheng An

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    这项研究引入了一种使用深度学习的自动化3D面部标志方法,用于在整形外科手术中更快,更准确的外围人体分析. 这种新的方法显著减少了3D面部建模中的手工劳动和错误.

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    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 整形外科 整形外科 整形外科

    背景情况:

    • 三维面部立体摄影仪在整形外科手术中非常有价值,用于规划和有效性评估.
    • 手动地标识别是耗时且容易出现错误的.
    • 自动化3D面部地标定位提供了效率和精度的改进.

    研究的目的:

    • 介绍一种新的深度学习方法,用于自动化3D周边地标注释.
    • 为了使3D面部模型的高效和精确的人类测量数据获取.

    主要方法:

    • 使用高分辨率网络将3D面部模型转换为2D图像,用于关键点检测.
    • 2D关键点坐标被映射回3D模型以确定3D地标坐标.
    • 该方法在120个模型上进行了训练,并在50个模型上进行了验证.

    主要成果:

    • 自动化方法实现了1.30mm的平均精度,用于地标检测.
    • 每个模型的处理时间平均为5.2秒.
    • 随后的测量显示线性分析的平均误差为0.87mm,角分析的平均误差为5.62°.

    结论:

    • 开发的自动化3D外周边标记方法对整形手术有效.
    • 它有助于快速,准确地进行唇形状学的人体测量分析.
    • 该工具支持需要精确面部测量的审美程序.