An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images

TL;DR

This paper introduces an automated method for detecting, identifying, and extracting 3D models of teeth from CBCT images, improving dental analysis by handling artifacts and complex structures efficiently.

Contribution

It presents a novel single-stage detection approach for 3D teeth segmentation from CBCT images, integrated into a dental analysis tool.

Findings

Effective detection and labeling of teeth in CBCT images

Successful creation of 3D tooth models from axial slices

Integration into a dental analysis software

Abstract

Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intricate to accomplish, especially when fillings and other restorations introduce artifacts. This paper proposes a method for automatically detecting, identifying, and extracting teeth from CBCT images. Our approach involves dividing the three-dimensional images into axial slices for image detection. Teeth are pinpointed and labeled using a single-stage object detector. Subsequently, bounding boxes are delineated and identified to create three-dimensional representations of each tooth. The proposed solution has been successfully integrated into the dental analysis tool Dentomo.