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Tooth Anatomy

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...

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Updated: Jan 6, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

An open deep learning-based framework and model for tooth instance segmentation in dental CBCT.

You Zhou¹, Yan Xu², Basel Khalil¹

¹Periodontology & Implant Dentistry, Faculty of Dentistry, The Prince Philip Dental Hospital, The University of Hong Kong, 34 Hospital Road, Sai Ying Pun, Hong Kong.

Clinical Oral Investigations

|September 25, 2025

Summary

This summary is machine-generated.

OraSeg, a new deep learning tool, offers accurate, one-click dental CBCT segmentation for 35 oral structures. This accessible solution aids dentists and researchers in diagnosis and digital dentistry adoption.

Keywords:

3D slicer Artificial intelligence Cone-beam computed tomography Instance labelling Maxillofacial imaging

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Area of Science:

Oral and Maxillofacial Radiology
Artificial Intelligence in Medicine
Medical Image Analysis

Background:

Current cone-beam computed tomography (CBCT) segmentation tools often lack accuracy, accessibility, and comprehensive anatomical coverage.
Precise segmentation of dental CBCT is crucial for diagnosis, treatment planning, and research.

Purpose of the Study:

To develop and validate OraSeg, a deep learning model for accurate tooth-level instance segmentation of dental CBCT images.
To create a user-friendly, one-click tool for non-expert users, enhancing accessibility for clinical and research applications.

Main Methods:

Construction of a densely annotated dental CBCT dataset covering 35 key oral anatomical structures.
Development of the OraSeg model using UNetR with Swin Transformer and spatial Mamba for multi-scale feature fusion.
Integration of OraSeg into the 3D Slicer platform for a one-click graphical user interface.

Main Results:

OraSeg achieved a Dice similarity coefficient of 0.8316 ± 0.0305 for CBCT instance segmentation, outperforming SwinUNETR and 3D U-Net.
The model demonstrated significant performance improvements in segmenting complex anatomical regions like apical areas, alveolar bone margins, and mandibular nerve canals.

Conclusions:

OraSeg provides an effective and accessible solution for instance segmentation of dental CBCT images.
The tool empowers dentists and researchers without AI expertise to perform one-click segmentation, supporting clinical diagnosis, education, and research.
OraSeg facilitates the integration of digital dentistry and precision medicine through user-friendly segmentation.