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Updated: Oct 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Caries segmentation on tooth X-ray images with a deep network.

Shunv Ying1, Benwu Wang2, Haihua Zhu1

  • 1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China.

Journal of Dentistry
|February 26, 2022
PubMed
Summary

A novel deep network accurately segments dental caries in X-ray images, outperforming existing methods. This advancement offers significant potential for automated clinical diagnosis and treatment planning.

Keywords:
Artificial intelligenceDeep learningDental cariesMedical imageSegmentationU-shaped networkX-ray

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Area of Science:

  • Biomedical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Deep learning shows promise in biomedical applications.
  • Accurate caries segmentation is crucial for dental diagnostics.

Purpose of the Study:

  • To propose a deep network for automatic caries segmentation in tooth X-ray images.
  • To enhance multi-scale and global feature extraction for improved segmentation accuracy.

Main Methods:

  • A novel deep network integrating U-shaped network characteristics, vision Transformer, dilated convolution, and feature pyramid fusion was developed.
  • The network was trained and evaluated on a self-collected dataset of tooth X-ray images.

Main Results:

  • The proposed network achieved an average dice similarity of 0.7487 and pixel classification precision of 0.7443.
  • Performance surpassed established networks like UNet, Trans-UNet, and Swin-UNet.

Conclusions:

  • The study successfully developed an effective deep network for automatic caries segmentation.
  • The findings highlight the potential clinical utility of this automated approach for dental diagnostics.