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A progressive framework for tooth and substructure segmentation from cone-beam CT images.

Minhui Tan1, Zhiming Cui2, Tao Zhong3

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China.

Computers in Biology and Medicine
|December 27, 2023
PubMed
Summary

This study introduces a novel deep learning framework for automatically segmenting 3D teeth and their substructures (enamel, pulp, dentin) from cone-beam computed tomography (CBCT) images, improving dental diagnostics.

Keywords:
CBCT imagesCenter clusteringHybrid featuresSkeletonTooth and substructure segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dental Technology

Background:

  • Accurate segmentation of teeth and substructures (enamel, pulp, dentin) from cone-beam computed tomography (CBCT) is crucial for digital dentistry.
  • Current CBCT segmentation methods show progress in tooth identification but lack substructure segmentation capabilities.

Purpose of the Study:

  • To develop a novel three-stage progressive deep-learning framework for automatic 3D tooth and substructure segmentation from CBCT images.
  • To focus on segmenting finer dental substructures like enamel, pulp, and dentin.

Main Methods:

  • A three-stage deep learning approach involving tooth detection via clustering and displacement vectors.
  • A tooth segmentation network utilizing an attention-based hybrid feature fusion mechanism for enhanced boundary and shape details.
  • Employing tooth skeleton as a guide for subsequent substructure segmentation.

Main Results:

  • The proposed algorithm was evaluated on a dataset of 314 patients.
  • Extensive comparison and ablation studies demonstrated superior segmentation performance of the developed approach.

Conclusions:

  • The developed method enables automatic segmentation of teeth and finer substructures from CBCT images.
  • This technique holds significant potential for clinical diagnosis and surgical treatment planning in dentistry.