Automatic tooth periodontal ligament segmentation of cone beam computed tomography based on instance segmentation network.

ABSTRACT:

Objective

The three-dimensional morphological structures of periodontal ligaments (PDLs) are important data for periodontal, orthodontic, prosthodontic, and implant interventions. This study aimed to employ a deep learning (DL) algorithm to segment the PDL automatically in cone-beam computed tomography (CBCT).

Method

This was a retrospective study. We randomly selected 389 patients and 1734 axial CBCT images from the CBCT database, and designed a fully automatic PDL segmentation computer-aided model based on instance segmentation Mask R-CNN network. The labels of the model training were 'teeth' and 'alveolar bone', and the 'PDL' is defined as the region where the 'teeth' and 'alveolar bone' overlap. The model's segmentation performance was evaluated using CBCT data from eigh

SUBMITTER: Su S 

PROVIDER: S-EPMC10827460 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature