Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning.

Accurate and automatic segmentation of individual tooth and root canal from cone-beam computed tomography (CBCT) images is an essential but challenging step for dental surgical planning. In this paper, we propose a novel framework, which consists of two neural networks, DentalNet and PulpNet, for efficient, precise, and fully automatic tooth instance segmentation and root canal segmentation from CBCT images. We first use the proposed DentalNet to achieve tooth instance segmentation and identification.

Applicability and accuracy of Demirjian and Willems methods in a population of Eastern Chinese subadults.

Background: To assess the validity of the Demirjian and Willems dental age estimation methods in a population of Eastern Chinese 11-18-year-old subadults.

Samples And Methods: A total of 1622 orthopantomograms (787 boys and 835 girls) aged from 11.00 to 18.