An open deep learning-based framework and model for tooth instance segmentation in dental CBCT.

ABSTRACT:

Objectives

Current dental CBCT segmentation tools often lack accuracy, accessibility, or comprehensive anatomical coverage. To address this, we constructed a densely annotated dental CBCT dataset and developed a deep learning model, OraSeg, for tooth-level instance segmentation, which is then deployed as a one-click tool and made freely accessible for non-commercial use.

Materials and methods

We established a standardized annotated dataset covering 35 key oral anatomical structures and employed UNetR as the backbone network, combining Swin Transformer and the spatial Mamba module for multi-scale residual feature fusion. The OralSeg model was designed and optimized for precise instance segmentation of dental CBCT images, and integrated into the 3D Slicer platform, providing a

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12464119 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature