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

Title
Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks
Authors
Lee J.-H.Yu H.-J.Kim M.-J.Kim J.-W.Choi J.
Ewha Authors
김민지김진우
SCOPUS Author ID
김민지scopus; 김진우scopus
Issue Date
2020
Journal Title
BMC Oral Health
ISSN
1472-6831JCR Link
Citation
BMC Oral Health vol. 20, no. 1
Keywords
Artificial intelligenceArtificial neural networksBayesian methodCephalometryDeep learningDental anatomyMachine visionOral &maxillofacial surgeryOrthodontic(s)OrthodonticsOrthognathic/orthognathic surgeryRadiography
Publisher
BioMed Central Ltd
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
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. © 2020 The Author(s).
DOI
10.1186/s12903-020-01256-7
Appears in Collections:
의과대학 > 의학과 > Journal papers
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