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在克罗地亚人口中使用适应的PointNet++网络对3D舌骨模型进行基于深度学习的性别估计

Ivan Jerković¹, Željana Bašić², Ivana Kružić¹

¹Faculty of Forensic Sciences, University of Split, Split, Croatia.

Scientific reports

|July 2, 2025

概括

深度学习使用CT扫描的3D舌骨模型准确估计性别. 这种方法有助于法医人类学,通过识别骨遗骸中的基于性别的形态模式.

科学领域:

法医人类学法医人类学
生物医学工程生物医学工程
计算机科学计算机科学

背景情况:

从骨遗骸中估计性别在法医人类学中至关重要.
状腺骨形态学为性别确定提供了潜在的可能性.
先进的计算方法可以分析复杂的3D骨架数据.

研究的目的:

开发和评估一个深度学习方法,使用3D状骨模型来估计性别.
评估深度学习模型的准确性和可解释性.
探索这种方法在法医人类学应用中的潜力.

主要方法:

利用来自于克罗地亚人口中202名个人 (101名男性,101名女性) 的计算机断层扫描 (CT) 获得的3D状骨模型.
将CT衍生网格转换为2048点云,用于与适应的PointNet++网络进行处理.
员工无监督集群和监督分类 (支持矢量机器) 用于性别估计.

主要成果:

无监督的聚类在识别基于性别的形态模式方面实现了87.10%的准确性.
监督分类在测试组上产生了88.71%的准确性 (MCC = 0.7746).
解释性分析显示出明显的形态差异:雄性具有更大的U形舌头;雌性具有更小,更开放的结构.

结论:

深度学习方法有效地捕捉了雄骨形态中的性别差异.
该方法具有数据效率和可解释性,显示了在有限的骨遗骸中估计性别的潜力.
这个计算工具为法医人类学和相关领域提供了实用的见解.

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