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

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. Pr

SUBMITTER: Lee JH 

PROVIDER: S-EPMC7541217 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature