Lightweight deep learning methods for panoramic dental X-ray image segmentation

Songyue Lin, Xuejiang Hao, Yan Liu, Dong Yan, Jianwei Liu, Mingjun Zhong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
3 Downloads (Pure)


Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters (∼ 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis.

Original languageEnglish
JournalNeural Computing and Applications
Publication statusPublished - 16 Dec 2022

Data Availability Statement

The data sets analyzed during the current study were published in [3] and are available in the following repository:


  • Artificial intelligence
  • Deep learning
  • Image segmentation
  • Lightweight neural network
  • Tooth segmentation
  • X-ray


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