STS-TransUNet: Semi-supervised Tooth Segmentation Transformer U-Net for dental panoramic image

Duolin Sun; Jianqing Wang; Zhaoyu Zuo; Yixiong Jia; Yimou Wang; Duolin Sun; Jianqing Wang; Zhaoyu Zuo; Yixiong Jia; Yimou Wang

doi:10.3934/mbe.2024104

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 2366-2384. doi: 10.3934/mbe.2024104

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STS-TransUNet: Semi-supervised Tooth Segmentation Transformer U-Net for dental panoramic image

1.
University of Science and Technology of China, Hefei, China
2.
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
3.
Hangzhou Sai Future Technology Co., Ltd, Hangzhou, China
4.
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Academic Editor: Junxin Chen
^† Co-First Authors: These two authors contributed equally.

Received: 16 November 2023 Revised: 22 December 2023 Accepted: 23 December 2023 Published: 15 January 2024

In this paper, we introduce a novel deep learning method for dental panoramic image segmentation, which is crucial in oral medicine and orthodontics for accurate diagnosis and treatment planning. Traditional methods often fail to effectively combine global and local context, and struggle with unlabeled data, limiting performance in varied clinical settings. We address these issues with an advanced TransUNet architecture, enhancing feature retention and utilization by connecting the input and output layers directly. Our architecture further employs spatial and channel attention mechanisms in the decoder segments for targeted region focus, and deep supervision techniques to overcome the vanishing gradient problem for more efficient training. Additionally, our network includes a self-learning algorithm using unlabeled data, boosting generalization capabilities. Named the Semi-supervised Tooth Segmentation Transformer U-Net (STS-TransUNet), our method demonstrated superior performance on the MICCAI STS-2D dataset, proving its effectiveness and robustness in tooth segmentation tasks.
- dental panoramic image,
- attention mechanisms,
- STS-TransUNet,
- self-training,
- semi-supervised
Citation: Duolin Sun, Jianqing Wang, Zhaoyu Zuo, Yixiong Jia, Yimou Wang. STS-TransUNet: Semi-supervised Tooth Segmentation Transformer U-Net for dental panoramic image[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2366-2384. doi: 10.3934/mbe.2024104

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Abstract

In this paper, we introduce a novel deep learning method for dental panoramic image segmentation, which is crucial in oral medicine and orthodontics for accurate diagnosis and treatment planning. Traditional methods often fail to effectively combine global and local context, and struggle with unlabeled data, limiting performance in varied clinical settings. We address these issues with an advanced TransUNet architecture, enhancing feature retention and utilization by connecting the input and output layers directly. Our architecture further employs spatial and channel attention mechanisms in the decoder segments for targeted region focus, and deep supervision techniques to overcome the vanishing gradient problem for more efficient training. Additionally, our network includes a self-learning algorithm using unlabeled data, boosting generalization capabilities. Named the Semi-supervised Tooth Segmentation Transformer U-Net (STS-TransUNet), our method demonstrated superior performance on the MICCAI STS-2D dataset, proving its effectiveness and robustness in tooth segmentation tasks.

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