ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images

TL;DR

ToothSegNet is a novel segmentation framework for CBCT images that improves tooth segmentation accuracy by training with simulated degraded images and employing a fusion and structural loss strategy.

Contribution

The paper introduces ToothSegNet, which incorporates degradation simulation and a fusion mechanism to enhance tooth segmentation in challenging CBCT images.

Findings

Outperforms state-of-the-art segmentation methods

Produces more precise tooth segmentation

Effectively handles image degradation issues

Abstract

In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments. Tooth segmentation from cone-beam computed tomography (CBCT) images is a crucial step in constructing the models. However, CBCT image quality problems such as metal artifacts and blurring caused by shooting equipment and patients' dental conditions make the segmentation difficult. In this paper, we propose ToothSegNet, a new framework which acquaints the segmentation model with generated degraded images during training. ToothSegNet merges the information of high and low quality images from the designed degradation simulation module using channel-wise cross fusion to reduce the semantic gap between encoder and decoder, and also refines the shape of tooth prediction through a structural constraint loss. Experimental results suggest that ToothSegNet produces more precise segmentation and outperforms the state-of-the-art medical image segmentation methods.