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Abstract
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.
Highlights
With its ease of use and portability, 3D imaging technology has transformed clinical diagnostic methods and research into the causes and consequences of morphological variation. 3D imaging systems have quick capture speeds, are minimally invasive, and provide researchers and clinicians with the ability to create detailed, comprehensive, and realistic images for use in assessing variation or planning treatments
All imaging systems have some technical error, an inherent level of variation introduced as a function of the hardware and software used by the camera, which cannot be reduced by the operator
Given the advancements in registration technology, expanding our analyses from a few dozen landmarks to several thousand, and the increase in opportunities for combined analyses of data collected with different imaging systems, we sought to explore: (1) the presence and extent of random artifactual variation originating from registering images using the MeshMonk non-rigid surface registration toolbox[25] as opposed to traditional human-placed landmarks; (2) the presence and extent of non-random variation as a result of technical error from the camera and participant movement during imaging; and (3) potential systematic biases in the manner in which images are photographed and represented by two different, commonly-used, camera systems: the 3dMDface and Vectra H1
Summary
With its ease of use and portability, 3D imaging technology has transformed clinical diagnostic methods and research into the causes and consequences of morphological variation. 3D imaging systems have quick capture speeds, are minimally invasive, and provide researchers and clinicians with the ability to create detailed, comprehensive, and realistic images for use in assessing variation or planning treatments. Given the advancements in registration technology, expanding our analyses from a few dozen landmarks to several thousand, and the increase in opportunities for combined analyses of data collected with different imaging systems, we sought to explore: (1) the presence and extent of random artifactual variation originating from registering images using the MeshMonk non-rigid surface registration toolbox[25] as opposed to traditional human-placed landmarks; (2) the presence and extent of non-random variation as a result of technical error from the camera and participant movement during imaging; and (3) potential systematic biases in the manner in which images are photographed and represented by two different, commonly-used, camera systems: the 3dMDface and Vectra H1. Form variability resulting from systematic differences between cameras was assessed by comparing the same individuals imaged with two different camera systems (‘camera error’; Fig. 1)
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