A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT

TL;DR

This paper introduces a multi-stage framework for 3D tooth segmentation in dental CBCT images, addressing data annotation challenges and domain shift issues, and demonstrating competitive performance in a semi-supervised segmentation challenge.

Contribution

The paper proposes a novel multi-stage framework that improves 3D tooth segmentation accuracy and generalization in CBCT images, especially under limited annotated data conditions.

Findings

Achieved third place in the STS-3D challenge.

Validated effectiveness through experiments on the validation set.

Outperformed other semi-supervised methods in accuracy.

Abstract

Cone beam computed tomography (CBCT) is a common way of diagnosing dental related diseases. Accurate segmentation of 3D tooth is of importance for the treatment. Although deep learning based methods have achieved convincing results in medical image processing, they need a large of annotated data for network training, making it very time-consuming in data collection and annotation. Besides, domain shift widely existing in the distribution of data acquired by different devices impacts severely the model generalization. To resolve the problem, we propose a multi-stage framework for 3D tooth segmentation in dental CBCT, which achieves the third place in the "Semi-supervised Teeth Segmentation" 3D (STS-3D) challenge. The experiments on validation set compared with other semi-supervised segmentation methods further indicate the validity of our approach.