THISNet: Tooth Instance Segmentation on 3D Dental Models via Highlighting Tooth Regions

• Published in: IEEE transactions on circuits and systems for video technology 2024-07, Vol.34 (7), p.5229-5241
• Main Authors: Li, Pengcheng, Gao, Chenqiang, Liu, Fangcen, Meng, Deyu, Yan, Yan
• Format: Article
• Language: English
• Subjects: 3D dental models; Computational modeling; Dental materials; Dentistry; highlighting; Instance segmentation; Labeling; object affinity; Solid modeling; Teeth; Three-dimensional displays; Tooth segmentation
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2023.3341805

Automatic tooth instance segmentation on 3D dental models is crucial for digitizing dental treatments and enabling computer-assisted treatment planning. However, It is challenging since the tight arrangement of dental structures and the consequential impact of dental ailments on their morphological characteristics. To address these challenges, we propose a novel method called THISNet. Unlike existing methods, THISNet focuses on highlighting tooth regions rather than relying on bounding box detection, leading to improved accuracy in tooth segmentation and labeling. By incorporating the highlighted tooth regions with a tooth object affinity module, our method effectively integrates global contextual information, considering the relationships between neighboring teeth and their surrounding structures. THISNet adopts an end-to-end learning approach, reducing complexity and enhancing segmentation efficiency compared to multi-stage training methods. Experimental results demonstrate the superiority of THISNet over existing approaches, highlighting its potential in various dental clinical applications.