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Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application.
Dan Luo1, Wei Zeng1, Jinlong Chen1
1The State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, China.
Frontiers in Medical Technology
|January 20, 2022
Summary
Deep learning significantly advances stomatological image segmentation. This review categorizes deep learning methods by data source, network, and task, aiding future research in dental AI.
Area of Science:
- Medical Image Analysis
- Artificial Intelligence in Dentistry
Background:
- Deep learning (DL) is revolutionizing medical image analysis, particularly in dentistry.
- Significant progress has been achieved in automatic segmentation of stomatological images using DL.
Purpose of the Study:
- To systematically review recent literature on DL-based segmentation methods for stomatological images.
- To analyze their clinical applications, advantages, and disadvantages.
- To identify challenges and future research directions.
Main Methods:
- Literature review categorizing methods by data sources (radiography, CT, intraoral scans).
- Analysis based on backbone networks (CNNs, Transformers) and task formulations (semantic, instance segmentation).
Main Results:
- Categorization of diverse data sources used in stomatological image segmentation.
- Distinction between Convolutional Neural Network (CNN) and Transformer-based backbone networks.
- Classification of segmentation tasks into semantic and instance segmentation.
Conclusions:
- Deep learning methods show great promise for automatic stomatological image segmentation.
- Further research is needed to address current challenges and enhance clinical applications.
