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Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At present, the mandible is commonly segmented by experienced doctors using manually or semi-automatic methods, which is time-consuming and has poor segmentation consistency. In addition, existing automatic segmentation methods still have problems such as region misjudgment, low accuracy, and time-consuming.

Methods

For these issues, an automatic mandibular segmentation method using 3d fully convolutional neural network based on densely connected atrous spatial pyramid pooling (DenseASPP) and attention gates (AG) was proposed in this paper. Firstly, the DenseASPP module was added to the network for extracting dense features at multiple scales. Thereafter, the AG module was applied in each skip connection to diminish irrelevant background information and make the network focus on segmentation regions. Finally, a loss function combining dice coefficient and focal loss was used to solve the imbalance among sample categories.

Results

Test results showed that the proposed network obtained a relatively good segmentation result, with a Dice score of 97.588 ± 0.425%, Intersection over Union of 95.293 ± 0.812%, sensitivity of 96.252 ± 1.106%, average surface distance of 0.065 ± 0.020 mm and 95% Hausdorff distance of 0.491 ± 0.021 mm in segmentation accuracy. The comparison with other segmentation networks showed that our network not only had a relatively high segmentation accuracy but also effectively reduced the network's misjudgment. Meantime, the surface distance error also showed that our segmentation results were relatively close to the ground truth.

Conclusion

The proposed network has better segmentation performance and realizes accurate and automatic segmentation of the mandible. Furthermore, its segmentation time is 50.43 s for one CT scan, which greatly improves the doctor's work efficiency. It will have practical significance in cranio-maxillofacial surgery in the future.

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Acknowledgements

This work was supported by grants from the National Key Research and Development Program of China (No. 2017YFB1302900), National Natural Science Foundation of China (81971709; M-0019; 82011530141), the Foundation of Science and Technology Commission of Shanghai Municipality (19510712200, 20490740700), Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research (ZH2018ZDA15; YG2019ZDA06; ZH2018QNA23), and 2020 Key Research project of Xiamen Municipal Government (3502Z20201030).

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Author notes

  1. Jiangchang Xu and Jiannan Liu have contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room 805, Dongchuan Road 800, Minhang District, Shanghai, 200240, China

    Jiangchang Xu, Dingzhong Zhang & Xiaojun Chen

  2. Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 639, ZhiZao JuRd, Shanghai, 200011, China

    Jiannan Liu, Zijie Zhou & Chenping Zhang

  3. Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  4. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

    Xiaojun Chen

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  1. Jiangchang Xu
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  2. Jiannan Liu
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  3. Dingzhong Zhang
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  4. Zijie Zhou
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  5. Xiaoyi Jiang
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  6. Chenping Zhang
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Corresponding authors

Correspondence to Chenping Zhang or Xiaojun Chen.

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Xu, J., Liu, J., Zhang, D. et al. Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates. Int J CARS 16, 1785–1794 (2021). https://doi.org/10.1007/s11548-021-02447-5

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  • DOI: https://doi.org/10.1007/s11548-021-02447-5

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