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

• Váldodahkkit: Jeong-Hoon Lee (Dahkki), Hee-Jin Yu (Dahkki), Min-ji Kim (Dahkki), Jin-Woo Kim (Dahkki), Jongeun Choi (Dahkki)
• Materiálatiipa: Girji
• Almmustuhtton: BMC, 2020-10-01T00:00:00Z.
• Fáttát: article
• Liŋkkat: Connect to this object online.
• Notes: 10.1186/s12903-020-01256-7; 1472-6831
• Keywords: Artificial neural networks; Bayesian method; Cephalometry; Orthodontics; Machine vision; Deep learning; Dentistry; RK1-715
• Source: BMC Oral Health, Vol 20, Iss 1, Pp 1-10 (2020)

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 post-processing 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.