Department of Anthropology, The Pennsylvania State University, University Park, PA, United States.
Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
Sci Rep. 2020 Mar 10;10(1):4443. doi: 10.1038/s41598-020-61333-3.
As technology advances and collaborations grow, our ability to finely quantify and explore morphological variation in 3D structures can enable important discoveries and insights into clinical, evolutionary, and genetic questions. However, it is critical to explore and understand the relative contribution of potential sources of error to the structures under study. In this study, we isolated the level of error in 3D facial images attributable to four sources, using the 3dMDface and Vectra H1 camera systems. When the two camera systems are used separately to image human participants, this analysis finds an upper bound of error potentially introduced by the use of the 3dMDface or Vectra H1 camera systems, in conjunction with the MeshMonk registration toolbox, at 0.44 mm and 0.40 mm, respectively. For studies using both camera systems, this upper bound increases to 0.85 mm, on average, and there are systematic differences in the representation of the eyelids, nostrils, and mouth by the two camera systems. Our results highlight the need for careful assessment of potential sources of error in 3D images, both in terms of magnitude and position, especially when dealing with very small measurements or performing many tests.
随着技术的进步和合作的增加，我们能够精细地定量和探索 3D 结构中的形态变化，这将为临床、进化和遗传问题的重要发现和见解提供帮助。然而，探索和理解潜在误差源对所研究结构的相对贡献至关重要。在这项研究中，我们使用 3dMDface 和 Vectra H1 相机系统，将 3D 面部图像的误差水平归因于四个来源。当分别使用两个相机系统对人类参与者进行成像时，这项分析发现，使用 3dMDface 或 Vectra H1 相机系统以及 MeshMonk 注册工具箱可能引入的误差上限分别为 0.44 毫米和 0.40 毫米。对于同时使用两个相机系统的研究，这个上限平均增加到 0.85 毫米，并且两个相机系统在眼皮、鼻孔和嘴巴的表现上存在系统差异。我们的研究结果强调了需要仔细评估 3D 图像中潜在误差源的大小和位置，尤其是在处理非常小的测量值或进行许多测试时。
