Automated integration of facial and intra-oral images of anterior teeth.,Computers in Biology and Medicine

当前位置： X-MOL 学术 › Comput. Biol. Med. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Automated integration of facial and intra-oral images of anterior teeth.
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2020-05-23 , DOI: 10.1016/j.compbiomed.2020.103794
Mengxun Li ₁ , Xiangyang Xu ₂ , Kumaradevan Punithakumar ₃ , Lawrence H Le ₃ , Neelambar Kaipatur ₄ , Bin Shi ₁

Affiliation

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.

中文翻译：

自动整合前牙的面部和口腔内图像。

背景与目的：数字笑容设计是牙医在实际治疗之前用于在计算机工作站上分析，设计和可视化治疗结果的技术。尽管这是数字笑容设计中的关键步骤，但在面部和口腔内图像中标记和整合信息的过程却很麻烦。因此，本研究旨在开发一种自动化的照片集成系统以促进此过程。

方法：

口腔内图像中的牙齿通过弯曲度进行区分，并使用主动轮廓模型进行精细分割。通过复杂的面部界标检测器算法检测面部关键点；然后，通过提取面部和口腔内照片中的牙齿轮廓，将这些关键点覆盖在相应的口腔内图像上。使用该系统，可以在口腔内图像中自动标记牙齿的宽高比，微笑线和面部中线。通过将分割算法应用于274个上颌前牙的50张图像，评估了分割算法的准确性。

结果：

所提出的算法在我们选择的图像集中识别出96.0％（263/274）的牙齿。然后将结果与通过对剩余的263个识别出的牙齿进行手动分割而获得的结果进行比较。在95％的置信区间内，Jaccard指数为0.928±0.081，平均距离为0.128±0.109 mm，结果与真实情况之间的Hausdorff距离为0.461±0.495 mm。