A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Tooth segmentation is an important aspect of virtual orthodontic systems. In some existing studies using deep learning-based tooth segmentation methods, the feature learning of point coordinate information and normal vector information is not effectively distinguished. This will lead to the feature...

Full description

Saved in:
Bibliographic Details
Published in:Healthcare (Basel) Vol. 10; no. 10; p. 2089
Main Authors: Ma, Tian, Yang, Yizhou, Zhai, Jiechen, Yang, Jiayi, Zhang, Jiehui
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-10-2022
MDPI
Subjects:
3D shape segmentation
Accuracy
Algorithms
Deep learning
Dentists
Design
geometric feature learning
Image segmentation
Machine learning
Medical examination
Methods
Morphology
Neural networks
Orthodontics
Technology application
Teeth
tooth segmentation
China
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tooth segmentation is an important aspect of virtual orthodontic systems. In some existing studies using deep learning-based tooth segmentation methods, the feature learning of point coordinate information and normal vector information is not effectively distinguished. This will lead to the feature information of these two methods not producing complementary intermingling. To address this problem, a tooth segmentation method based on multiple geometric feature learning is proposed in this paper. First, the spatial transformation (T-Net) module is used to complete the alignment of dental model mesh features. Second, a multiple geometric feature learning module is designed to encode and enhance the centroid coordinates and normal vectors of each triangular mesh to highlight the differences between geometric features of different meshes. Finally, for local to global fusion features, feature downscaling and channel optimization are accomplished layer by layer using multilayer perceptron (MLP) and efficient channel attention (ECA). The experimental results show that our algorithm achieves better accuracy and efficiency of tooth segmentation and can assist dentists in their treatment work.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare10102089