A comparison of U-Net series for teeth segmentation in CBCT images

Accurately segmenting complete teeth from CBCT images is not only crucial in the field of orthodontics but also holds significant importance in forensic science. However, fully automated teeth segmentation is challenging due to the tight interconnection of teeth and the complexity of their arrangement, as well as the difficulty in distinguishing them from the surrounding alveolar bone due to similar densities. Currently, U-Net-based approaches have demonstrated remarkable success across a spectrum of medical image processing tasks, particularly in the task of segmentation. This work compares some U-Net-based segmentation methods (U-Net, U-Net++, U²-Net, nnU-Net, and TransUNet) on clinical teeth segmentation. We assess the enhancements introduced by these networks over the original U-Net and validate their performance on the identical dataset. Experimental results, both qualitative and quantitative, reveal that all methods perform well, with TransUNet demonstrating the best performance, achieving a Dice coefficient of 0.9364. Notably, U-Net, serving as the foundational model, outperforms U-Net++ and U²-Net, highlighting its robust generalization capability.