Next Article in Journal
FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
Previous Article in Journal
Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot
Article

Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images

1
Biomedical Engineering Graduate Program, University of Calgary, Alberta, AB T2N 4N1, Canada
2
Department of Radiology, Alberta Children’s Hospital Research Institute and Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Alberta, AB T2N 4N1, Canada
3
Department of Cell Biology and Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Alberta, AB T2N 4N1, Canada
4
Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, CA 94143, USA
5
Department of Medical Genetics, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Alberta, AB T2N 4N1, Canada
6
Human Medical Genetics and Genomics Program and Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3171; https://doi.org/10.3390/s20113171
Received: 29 April 2020 / Revised: 25 May 2020 / Accepted: 29 May 2020 / Published: 3 June 2020
(This article belongs to the Section Intelligent Sensors)
3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects. View Full-Text
Keywords: facial landmarking; genetic syndrome; 3D surface scan facial landmarking; genetic syndrome; 3D surface scan
Show Figures

Figure 1

MDPI and ACS Style

Bannister, J.J.; Crites, S.R.; Aponte, J.D.; Katz, D.C.; Wilms, M.; Klein, O.D.; Bernier, F.P.J.; Spritz, R.A.; Hallgrímsson, B.; Forkert, N.D. Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images. Sensors 2020, 20, 3171. https://doi.org/10.3390/s20113171

AMA Style

Bannister JJ, Crites SR, Aponte JD, Katz DC, Wilms M, Klein OD, Bernier FPJ, Spritz RA, Hallgrímsson B, Forkert ND. Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images. Sensors. 2020; 20(11):3171. https://doi.org/10.3390/s20113171

Chicago/Turabian Style

Bannister, Jordan J., Sebastian R. Crites, J. D. Aponte, David C. Katz, Matthias Wilms, Ophir D. Klein, Francois P.J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, and Nils D. Forkert. 2020. "Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images" Sensors 20, no. 11: 3171. https://doi.org/10.3390/s20113171

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop