Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks
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RESEARCH ARTICLE
Open Access
Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks Jeong-Hoon Lee1†, Hee-Jin Yu1†, Min-ji Kim2, Jin-Woo Kim3* and Jongeun Choi1*
Abstract Background: Despite 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). Methods: We 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 postprocessing methods with respect to pixel probabilities and uncertainties. Results: Our 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. Conclusion: Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education. Keywords: Artificial neural networks, Bayesian method, Cephalometry, Orthodontics, Machine vision, Deep learning, Artificial intelligence, Orthodontic(s), Radiography, Orthognathic/orthognathic surgery, Oral & maxillofacial surgery, Dental anatomy
* Correspondence: [email protected]; [email protected]; [email protected] † Jeong-Hoon Lee and Hee-Jin Yu contributed equally to this work. 3 Department of Oral and Maxillofacial Surgery, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, Seoul 07985, Republic of Korea 1 School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul 03722, Republic of Korea Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party materia
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