  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.
