ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
This addresses a domain-specific problem in computer-assisted orthodontics for constructing 3D tooth models, with incremental improvements in segmentation accuracy.
The paper tackles tooth segmentation from CBCT images, which is hindered by quality issues like metal artifacts and blurring, and proposes ToothSegNet, a framework that uses generated degraded images during training to improve segmentation, resulting in more precise segmentation that outperforms state-of-the-art methods.
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.