Abstract
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.
Highlights
Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing
What makes this even more challenging is that a human skull is a highly sophisticated 3D object, whereas in a lateral cephalogram which has its model projected onto a sagittal plane, causes cubic features of the direction perpendicular to the plane to be overlapped [6, 7]
We propose the novel framework for locating cephalometric landmarks with confidence regions based on uncertainties using Bayesian Convolutional Neural Networks (BCNN) [22]
Summary
Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. Using standardized cephalometric x-ray, predefined anatomic landmarks are marked so that various orthodontic and facial morphometric analyses can be applied for the diagnosis and treatment planning. Despite the several methodological limitations such as nonlinear magnification and distortion of images, its integral role in orthodontics, as well as orthognathic and facial plastic surgery is indisputable [1,2,3]. The accuracy of marked cephalometric landmarks can affect the results of the clinical performance of analyses and resulting treatment decisions [4, 5]. What makes this even more challenging is that a human skull is a highly sophisticated 3D object, whereas in a lateral cephalogram which has its model projected onto a sagittal plane, causes cubic features of the direction perpendicular to the plane to be overlapped [6, 7]
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