Teeth segmentation from bite-wing X-Ray images by integrating nested dual UNet with swin transformers
Publication Type
Conference Proceeding Article
Publication Date
1-2025
Abstract
In medical practice, the precision of image segmentation is crucial for diagnosis and treatment evaluations. Specifically, in dentistry, accurate teeth segmentation from bite-wing images is important for automatic and objective evaluations of root canal treatments. This study introduces Swin−U2Net, a model merging the nested dual UNet with residual U-block and Swin Transformers. It combines the local feature extraction capability of the former and the global attention and context understanding of the latter. It has been evaluated for tooth root segmentation using 500 bite-wing dental x-ray images obtained from a root canal treatment clinic. It achieved the best segmentation outcome in terms of Intersection over Union (IOU) and the third best result in terms of Dice Similarity Coefficient (DSC) with the second least amount of network parameters among six UNet-like models, thus it is effective and efficient.
Keywords
Image segmentation, Irrigation, Medical diagnostic imaging, Teeth segmentation, Feature extraction, Dentistry, X-ray imaging
Discipline
Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization; Software and Cyber-Physical Systems
Publication
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2024) : Kuching, Malaysia, October 6-10
First Page
4548
Last Page
4553
Identifier
10.1109/SMC54092.2024.10831070
Publisher
IEEE
City or Country
Kuching. Malaysia
Citation
1
Additional URL
https://doi.org/10.1109/SMC54092.2024.10831070
