Automated integration of facial and intra-oral images of anterior teeth. (July 2020)

• Record Type: Journal Article
• Authors: Li, Mengxun; Xu, Xiangyang; Punithakumar, Kumaradevan; Le, Lawrence H.; Kaipatur, Neelambar; Shi, Bin
• Is Part Of: Computers in biology and medicine. Volume 122(2020)
• Publication Date: 2020-07
• Subjects: Aesthetic analysis -- Digital smile design -- Intra-oral image -- Image integration -- Tooth segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103794
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and Objective: Digital smile design is the technique that dentists use to analyze, design, and visualize therapeutic results on a computing workstation prior to actual treatment. Despite it being a crucial step in digital smile design, the process of labeling and integrating the information in facial and intra-oral images is laborious. Therefore, this study aims to develop an automated photo integrating system to facilitate this process. Methods: The teeth in intra-oral images were distinguished by their curvature and finely segmented using an active contour model. The facial keypoints were detected by a sophisticated facial landmark detector algorithm; these keypoints were then overlaid on the corresponding intra-oral image by extracting the contour of the teeth in the facial and intra-oral photographs. With this system, the tooth width-to-height ratios, smile line, and facial midline were automatically marked in the intra-oral image. The accuracy of the proposed segmentation algorithm was evaluated by applying it to 50 images with 274 maxillary anterior teeth. Results: The proposed algorithm recognized 96.0% (263/274) of teeth in our selected image set. The results were then compared to those obtained by applying manual segmentation to the remaining 263 recognized teeth. With a 95% confidence interval, a Jaccard index of 0.928 ± 0.081, average distance of 0.128 ± 0.109 mm, and Hausdorff distance between the results and ground truth of 0.461 ± 0.495 mm were achieved. Conclusions: The results of this study show that the proposed automated system can eliminate the need for dentists to employ a laborious image integration process. It also has the potential for broad applicability in the field of dentistry. Graphical abstract: Highlights: Automated integration of anterior teeth images from facial and intra-oral photos. Automated integration of facial and intra-oral information to facilitate aesthetic analysis. The system relies on ROI detection, tooth segmentation, and registration. A selected set of 274 maxillary anterior teeth was used for validation. Tooth segmentation yielded a mean Jaccard index of 0.928.