Mengxun Li1, Xiangyang Xu2, Kumaradevan Punithakumar3, Lawrence H Le3, Neelambar Kaipatur4, Bin Shi5. 1. Department of Implantology, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address: mengxunli@whu.edu.cn. 2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. 3. Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton AB, Canada. 4. School of Dentistry, University of Alberta, Edmonton, AB, Canada. 5. Department of Implantology, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address: shibin_dentist@whu.edu.cn.
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.
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.
Authors: Naseer Ahmed; Maria Shakoor Abbasi; Filza Zuberi; Warisha Qamar; Mohamad Syahrizal Bin Halim; Afsheen Maqsood; Mohammad Khursheed Alam Journal: Biomed Res Int Date: 2021-06-22 Impact factor: 3.411