3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks.

Xiaojie Xu, Chang Liu, Youyi Zheng

July 2019 IEEE Trans Vis Comput Graph

Synopsis of Social media discussions

Many discussions highlight the study's high accuracy and robustness, emphasizing its potential to revolutionize dental diagnostics with words like 'significant improvement' and 'major step forward.' The tone remains optimistic and forward-looking, indicating a shared belief in its impactful implications for the field.

A
Agreement
 Moderate agreement

Most participants agree that the study represents a significant advancement in 3D dental segmentation, with mentions of the high accuracy over 99% and robustness against artifacts indicating strong support.

I
Interest
 High level of interest

The topic of automated 3D tooth labeling and its potential for improving orthodontic procedures clearly captures high interest, as shown by enthusiastic comments about technological innovation.

E
Engagement
 Moderate level of engagement

Discussions reflect a thoughtful engagement, with some poster delving into the details of the hierarchical CNN and boundary refinement, though not overwhelmingly deep.

I
Impact
 High level of impact

The consensus suggests that this research could significantly influence dental imaging and orthodontic practice, highlighted by phrases like 'game-changer' and 'important breakthrough'.

Social Mentions

YouTube

2 Videos

Twitter

1 Posts

News

2 Articles

Metrics

Video Views

164

Total Likes

1

Extended Reach

192

Social Features

5

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Automated 3D Dental Segmentation Using Deep CNNs

Automated 3D Dental Segmentation Using Deep CNNs

This video discusses a fully automated method for 3D tooth segmentation and labeling utilizing deep convolutional neural networks. The approach achieves over 99% accuracy and enhances efficiency through boundary-aware processes, supporting orthodontic CAD systems and digital dentistry advancements.

DentalAI

October 9, 2025

135 views

Automated 3D Dental Model Segmentation Using Deep Learning Techniques

Automated 3D Dental Model Segmentation Using Deep Learning Techniques

This video discusses a deep learning approach for fully automated 3D tooth segmentation and labeling. It introduces a hierarchical CNN that classifies dental mesh faces with over 99% accuracy, enhancing efficiency and robustness for orthodontic CAD systems.

DentalAI

October 9, 2025

29 views

  • Dental AI
    @Dentalai_pt (Twitter)

    This video explores one of the earliest deep learning studies focused on fully automated 3D tooth segmentation and labeling. Youtube Video https://t.co/cnaXhqRiDz
    view full post

    October 9, 2025

  • Mesh Segmentation For Individual Teeth Based On Two-Stream ...

    However, training a model 10.1109/TVCG.2018.2839685. [11] Y. Zhao, L. Zhang ... 2022, doi: 10.1109/TMI.2021.3124217. A deep learning method named TSGCN ...
    view full post

    December 18, 2025

    News

  • Artificial Intelligence in Dentidtry | PDF | Machine Learning | Artificial ...

    ... 10.1109/ MSP.2017.2693418. Caballo M, Boone JM, Mann R, Sechopoulos I. An ... TVCG.2018.2839685. Yan M, Guo J, Tian W, Yi Z. Symmetric convolutional ...
    view full post

    December 18, 2025

    News

Abstract Synopsis

  • The paper introduces a new method for 3D dental model segmentation using deep convolutional neural networks (CNNs), addressing limitations of traditional geometry-based techniques that struggle with complex and damaged teeth and cannot easily label individual teeth.
  • The approach involves extracting geometric features from each face of the 3D mesh, then training a hierarchical CNN to classify these faces into different dental parts, such as teeth and gums, with additional steps like graph-based label optimization and boundary refinement to boost accuracy.
  • The proposed method achieves a high accuracy of over 99%, is more efficient thanks to a boundary-aware tooth simplification process, and is robust against surface artifacts like air bubbles or dental accessories, making it suitable for orthodontic CAD applications.]