A Global-Local Attention Model for 3D Point Cloud Segmentation in Intraoral Scanning: A Novel Approach.

Journal: Bioengineering (Basel, Switzerland)
Published Date: May 11, 2025

Abstract

Intraoral scanners (IOS) provide high-precision 3D data of teeth and gingiva, critical for personalized orthodontic diagnosis and treatment planning. However, traditional segmentation methods exhibit reduced performance with complex dental structures, such as crowded, missing, or irregular teeth, constraining their clinical applicability. This study aims to develop an advanced 3D point cloud segmentation model to enhance the automated processing of IOS data in intricate orthodontic scenarios. A 3D point cloud segmentation model was developed, incorporating relative coordinate encoding, Transformer-based self-attention, and attention pooling mechanisms. This design optimizes the extraction of local geometric features and long-range dependencies while maintaining a balance between segmentation accuracy and computational efficiency. Training and evaluation were conducted using internal and external orthodontic datasets. The model achieved a mean Intersection over Union (IoU) of 92.14% on the internal dataset and 91.73% on the external dataset, with Dice coefficients consistently surpassing those of established models, including PointNet++, TSGCN, and PointTransformer, demonstrating superior segmentation accuracy and robust generalization. The model significantly enhances tooth segmentation accuracy in complex orthodontic scenarios, such as crowded or irregular dentitions, enabling orthodontists to formulate treatment plans and simulate outcomes with greater precision-for example, optimizing clear aligner design or improving tooth arrangement efficiency. Its computational efficiency supports clinical applicability without excessive resource consumption. However, due to the limited sample size and potential influences from advancements in IOS technology, the model's generalizability requires further clinical testing and optimization in real-world orthodontic settings.

Authors

  • Haiwen Chen
    College of Computer, National University of Defense Technology, Changsha, China.
  • Yuan Qin
    College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China.
  • Baoning Liu
    School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
  • Houzhuo Luo
    State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthodontics, School of Stomatology, The Fourth Military Medical University, Xi'an 710032, China.
  • Ruyue Qiang
    State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthodontics, School of Stomatology, The Fourth Military Medical University, Xi'an 710032, China.
  • Yanni Meng
    Department of Anesthesiology, Shaanxi Provincial People's Hospital, Xi'an 710068, China.
  • Zhi Liu
  • Yanning Ma
    Department of Orthodontics, School of Stomatology, State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, Air Force Medical University, 710032 Xi'an, Shaanxi, China.
  • Zuolin Jin
    Department of Orthodontics, School of Stomatology, State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, Air Force Medical University, 710032 Xi'an, Shaanxi, China.

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