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An interactive geometric technique for upper and lower teeth segmentation.

Binh Huy Le1, Zhigang Deng, James Xia

  • 1Department of Computer Science, University of Houston, Houston, Texas, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 30, 2010
PubMed
Summary
This summary is machine-generated.

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Tooth Anatomy01:21

Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or grinding food.

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This study introduces an interactive method for segmenting upper and lower teeth from 3D dental models. The novel approach significantly reduces manual effort while maintaining high accuracy in dental segmentation.

Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Computational Geometry

Background:

  • Automated dental segmentation is challenging due to complex tooth shapes.
  • Manual segmentation is time-consuming and labor-intensive.
  • Existing interactive methods may lack efficiency and precision.

Purpose of the Study:

  • To develop a novel interactive method for segmenting upper and lower teeth.
  • To improve the efficiency and accuracy of dental model segmentation.
  • To reduce user intervention in the segmentation process.

Main Methods:

  • Reconstruction of 3D models from dental CT images.
  • Curvature estimation on 3D triangular mesh.
  • Interactive segmentation path planning using shortest path algorithms.

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  • Geometric cutting of 3D models via graph cut algorithms.

    • Main Results:

    • The interactive method achieved an average discrepancy of 0.29 mm compared to manual segmentation.
    • Demonstrated significant reduction in manual effort for users.
    • Validated accuracy and efficiency against ground truth and state-of-the-art algorithms.

    Conclusions:

    • The proposed interactive segmentation method offers a viable solution for complex dental models.
    • It balances user effort reduction with acceptable segmentation quality.
    • This technique has potential applications in digital dentistry and surgical planning.