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In computer-assisted orthodontics, three-dimensional tooth models arerequired for many medical treatments. Tooth segmentation from cone-beamcomputed tomography (CBCT) images is a crucial step in constructing the models.However, CBCT image quality problems such as metal artifacts and blurringcaused by shooting equipment and patients' dental conditions make thesegmentation difficult. In this paper, we propose ToothSegNet, a new frameworkwhich acquaints the segmentation model with generated degraded images duringtraining. ToothSegNet merges the information of high and low quality imagesfrom the designed degradation simulation module using channel-wise cross fusionto reduce the semantic gap between encoder and decoder, and also refines theshape of tooth prediction through a structural constraint loss. Experimentalresults suggest that ToothSegNet produces more precise segmentation andoutperforms the state-of-the-art medical image segmentation methods.

ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images