Dataset Information

Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.

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.

SUBMITTER: White JD

PROVIDER: S-EPMC7064576 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.

White Julie D JD Ortega-Castrillon Alejandra A Virgo Ciara C Indencleef Karlijne K Hoskens Hanne H Shriver Mark D MD Claes Peter P

Scientific reports 20200310 1

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 came ...[more]

PMID: 32157192

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Project description:IntroductionFacial phenotype is influenced by genes and environment; however, little is known about their relative contributions to normal facial morphology. The aim of this study was to assess the relative genetic and environmental contributions to facial morphological variation using a three-dimensional (3D) population-based approach and the classical twin study design.Materials and methods3D facial images of 1380 female twins from the TwinsUK Registry database were used. All faces were landmarked, by manually placing 37 landmark points, and Procrustes registered. Three groups of traits were extracted and analysed: 19 principal components (uPC) and 23 principal components (sPC), derived from the unscaled and scaled landmark configurations respectively, and 1275 linear distances measured between 51 landmarks (37 manually identified and 14 automatically calculated). The intraclass correlation coefficients, rMZ and rDZ, broad-sense heritability (h2), common (c2) and unique (e2) environment contributions were calculated for all traits for the monozygotic (MZ) and dizygotic (DZ) twins.ResultsHeritability of 13 uPC and 17 sPC reached statistical significance, with h2 ranging from 38.8% to 78.5% in the former and 30.5% to 84.8% in the latter group. Also, 1222 distances showed evidence of genetic control. Common environment contributed to one PC in both groups and 53 linear distances (4.3%). Unique environment contributed to 17 uPC and 20 sPC and 1245 distances.ConclusionsGenetic factors can explain more than 70% of the phenotypic facial variation in facial size, nose (width, prominence and height), lips prominence and inter-ocular distance. A few traits have shown potential dominant genetic influence: the prominence and height of the nose, the lower lip prominence in relation to the chin and upper lip philtrum length. Environmental contribution to facial variation seems to be the greatest for the mandibular ramus height and horizontal facial asymmetry.

Project description:ObjectiveThe intra-parotid facial nerve (FN) can be visualized using three-dimensional double-echo steady-state water-excitation sequence magnetic resonance imaging (3D-DESS-WE-MRI). However, the clinical impact of FN imaging using 3D-DESS-WE-MRI before parotidectomy has not yet been explored. We compared the clinical outcomes of parotidectomy in patients with and without preoperative 3D-DESS-WE-MRI.Materials and methodsThis prospective, non-randomized, single-institution study included 296 adult patients who underwent parotidectomy for parotid tumors, excluding superficial and mobile tumors. Preoperative evaluation with 3D-DESS-WE-MRI was performed in 122 patients, and not performed in 174 patients. FN visibility and tumor location relative to FN on 3D-DESS-WE-MRI were evaluated in 120 patients. Rates of FN palsy (FNP) and operation times were compared between patients with and without 3D-DESS-WE-MRI; propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to adjust for surgical and tumor factors.ResultsThe main trunk, temporofacial branch, and cervicofacial branch of the intra-parotid FN were identified using 3D-DESS-WE-MRI in approximately 97.5% (117/120), 44.2% (53/120), and 25.0% (30/120) of cases, respectively. The tumor location relative to FN, as assessed on magnetic resonance imaging, concurred with surgical findings in 90.8% (109/120) of cases. Rates of temporary and permanent FNP did not vary between patients with and without 3D-DESS-WE-MRI according to PSM (odds ratio, 2.29 [95% confidence interval {CI} 0.64-8.25] and 2.02 [95% CI: 0.32-12.90], respectively) and IPTW (odds ratio, 1.76 [95% CI: 0.19-16.75] and 1.94 [95% CI: 0.20-18.49], respectively). Conversely, operation time for surgical identification of FN was significantly shorter with 3D-DESS-WE-MRI (median, 25 vs. 35 min for PSM and 25 vs. 30 min for IPTW, P < 0.001).ConclusionPreoperative FN imaging with 3D-DESS-WE-MRI facilitated anatomical identification of FN and its relationship to the tumor during parotidectomy. This modality reduced operation time for FN identification, but did not significantly affect postoperative FNP rates.

Dataset Information

Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.

Publications

Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.

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