Abstract
Automatic and accurate individual tooth segmentation from cone-beam computed tomography images provides important assistance for computer-aided analysis in dentistry. Many previous studies on this task employ multi-stage strategy similar to instance segmentation in natural images. Although these methods can provide considerable segmentation results, they are dependent on complex training processes, some even in need of tuning hyperparameters for clustering. Meanwhile, due to the difference of strategy from other medical image segmentation, it is difficult for these methods to be extended to the segmentation from CBCT images in other human organs. In this paper, we present a novel method to train the network in only one stage with satisfactory result. The main componets of our method are an improved U-net like network and post refinement for tooth boundary. The proposed network is designed to conduct two different works in parallel. One is to directly predict the individual tooth segmentation while the other is to generate an offset map for the refinement. Besides, in order to improve the accuracy of tooth boundary segmentation, a boundary-aware loss is also applied in our method. Comparative experiments and ablation analysis show that our approach achieves state-of-the-art segmentation performance.
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Acknowledgements
This work was supported part by the National Natural Science Foundation of China (NSFC) under Grant No. 61876224, and part by the ‘QingTai Digital Intelligence Integration’ Collaborative Innovation Project of the Science and Technology Development Center of the Ministry of Education under Grant No. 2020QT16.
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Xu, Y., Zhang, M., Huang, S., Zhang, D. (2023). Tooth Segmentation from Cone-Beam CT Images Through Boundary Refinement. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_16
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