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

Fan Zhang; Linya Zheng; Chen Lin; Liping Huang; Yuming Bai; Yinran Chen; Xiongbiao Luo

doi:10.1117/12.3006464

2 April 2024 A comparison of U-Net series for teeth segmentation in CBCT images

Fan Zhang, Linya Zheng, Chen Lin, Liping Huang, Yuming Bai, Yinran Chen, Xiongbiao Luo

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129263D (2024) https://doi.org/10.1117/12.3006464
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Zhang, Linya Zheng, Chen Lin, Liping Huang, Yuming Bai, Yinran Chen, and Xiongbiao Luo "A comparison of U-Net series for teeth segmentation in CBCT images", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129263D (2 April 2024); https://doi.org/10.1117/12.3006464

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