Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

BMC Oral Health

Jin-Woo Kim¹  Min-ji Kim²  Hee-Jin Yu³  Jongeun Choi³  Jeong-Hoon Lee³ 

[1] Department of Oral and Maxillofacial Surgery, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, 07985, Seoul, Republic of Korea;Department of Orthodontics, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, 07985, Seoul, Republic of Korea;School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, 03722, Seoul, Republic of Korea;

关键词: Artificial neural networks;    Bayesian method;    Cephalometry;    Orthodontics;    Machine vision;    Deep learning;    Artificial intelligence;    Orthodontic(s);    Radiography;    Orthognathic/orthognathic surgery;    Oral & maxillofacial surgery;    Dental anatomy;   

DOI  :  10.1186/s12903-020-01256-7

来源: Springer

【 摘 要 】

BackgroundDespite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN).MethodsWe have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties.ResultsOur framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions.ConclusionOur framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

【 授权许可】

CC BY