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A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks

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Abstract

Panoramic radiography imaging plays a crucial role in the diagnostic process of dental diseases. However, current artificial intelligence research datasets for panoramic radiography dental image processing are often limited to single-center and single-task scenarios, making it difficult to generalize their results. To address this, we present a multi-center, multi-task labeled dataset. In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People’s Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specifically, it comprises 2555 images related to impacted teeth, 2735 images related to periodontitis, and 1246 images related to dental caries. In order to evaluate the performance of the dataset, we conducted benchmark tests for segmentation and classification tasks on our dataset. The results show that the presented dataset could be effectively used for benchmarking segmentation and classification tasks critical to the diagnosis of dental diseases. To request our multi-center dataset, please visit the address: https://github.com/qinxin99/qinxini.

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Data Availability

This dataset is made freely available and can be accessed on the GitHub repository - https://github.com/qinxin99/qinxini. Please see Table 2 for details to the data.

References

  1. Dhake T, Ansari N. A Survey on Dental Disease Detection Based on Deep Learning Algorithm Performance using Various Radiographs[C]//2022 5th International Conference on Advances in Science and Technology (ICAST). IEEE, 2022: 291–296.

  2. Chen H, Zhang K, Lyu P, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films[J]. Scientific reports, 2019, 9(1): 3840.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Oktay A B. Tooth detection with convolutional neural networks[C]//2017 Medical Technologies National Congress (TIPTEKNO). IEEE, 2017: 1–4.

  4. Muramatsu C, Morishita T, Takahashi R, et al. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data[J]. Oral Radiology, 2021, 37: 13-19.

    Article  PubMed  Google Scholar 

  5. Jader G, Fontineli J, Ruiz M, et al. Deep instance segmentation of teeth in panoramic X-ray images[C]//2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018: 400–407.

  6. Tuzoff D V, Tuzova L N, Bornstein M M, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks[J]. Dentomaxillofacial Radiology, 2019, 48(4): 20180051.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography[J]. Oral Radiology, 2020, 36: 337-343.

    Article  PubMed  Google Scholar 

  8. Panetta K, Rajendran R, Ramesh A, et al. Tufts dental database: a multimodal panoramic x-ray dataset for benchmarking diagnostic systems[J]. IEEE journal of biomedical and health informatics, 2021, 26(4): 1650-1659.

    Article  Google Scholar 

  9. Rubiu G, Bologna M, Cellina M, et al. Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network[J]. Applied Sciences, 2023, 13(13): 7947.

    Article  CAS  Google Scholar 

  10. Ness G M, Blakey G H, Hechler B L. Impacted teeth[J]. Peterson’s principles of oral and maxillofacial surgery, 2022: 131–169.

  11. Schwendicke F, Golla T, Dreher M, et al. Convolutional neural networks for dental image diagnostics: A scoping review[J]. Journal of dentistry, 2019, 91: 103226.

    Article  PubMed  Google Scholar 

  12. O Ronneberger P Fischer T Brox U-net: Convolutional networks for biomedical image segmentation[C] Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 Springer International Publishing 2015 234 241

  13. Chen J, Lu Y, Yu Q, et al. Transunet: Transformers make strong encoders for medical image segmentation[J]. arXiv preprint arXiv:2102.04306, 2021.

  14. Pang S, Du A, Orgun M A, et al. Tumor attention networks: Better feature selection, better tumor segmentation[J]. Neural Networks, 2021, 140: 203-222.

    Article  PubMed  Google Scholar 

  15. Gu Z, Cheng J, Fu H, et al. Ce-net: Context encoder network for 2d medical image segmentation[J]. IEEE transactions on medical imaging, 2019, 38(10): 2281-2292.

    Article  PubMed  Google Scholar 

  16. Lin A, Chen B, Xu J, et al. Ds-transunet: Dual swin transformer u-net for medical image segmentation[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-15.

    Google Scholar 

  17. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

    Article  Google Scholar 

  18. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.

  19. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1–9.

  20. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770–778.

  21. Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3–1

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Acknowledgements

We acknowledge all dentists who assisted us with the manual annotations.

Funding

This work was supported by: Innovative Research Group Project of the National Natural Science Foundation of China,81571836,Jie liu,KKA309004533,Jie liu,Key Research and Development Program of Ningxia,2023BEG02036. (1) National Natural Science Foundation of China, Project number: 81571836. (2) Beijing Jiaotong University, Project number: KKA309004533, 2006XM006, JS2002J0160, JS2002J0080. (3) Key Research and Development Plan of Ningxia Autonomous Region, 2023BEG02036

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Authors and Affiliations

  1. Department of Oral and Maxillofacial Surgery, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Xiang Li, Jingjing Hu & Zhongwei Zhou

  2. Ningxia Key Laboratory of Oral Diseases Research, Yinchuan, 750004, Ningxia, China

    Xiang Li, Jingjing Hu & Zhongwei Zhou

  3. School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232000, China

    Xiang Li

  4. The School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Xuan Ma, Yibai Zhao & Jie Liu

  5. Department of Oral and Maxillofacial Surgery, Shanghai Stomatological Hospital and School of Stomatology, Fudan University, Shanghai, 200001, China

    Zhicheng Yang & Zhongwei Zhou

  6. Shanghai Stomatological Hospital, Fudan University, Shanghai, 200001, China

    Fangkai Han

  7. School of Chips, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

    Jie Zhang

  8. College of Science, Beijing Forestry University, Beijing, 100083, China

    Weifan Liu

Authors
  1. Xiang Li
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  2. Xuan Ma
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  9. Weifan Liu
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  10. Zhongwei Zhou
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Corresponding authors

Correspondence to Jie Liu, Jie Zhang or Zhongwei Zhou.

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Ethics Approval

The acquisition process of this dataset is fully ethical and has been approved by the Medical Ethics Review Committee of the General Hospital of Ningxia Medical University, with the number KYLL-2021066.

Consent to Participate and for Publication

Informed consent was obtained from all individual participants who contributed to the dental panoramic radiography image dataset.

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The authors declare no competing interests.

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Li, X., Ma, X., Zhao, Y. et al. A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks. J Digit Imaging. Inform. med. 37, 831–841 (2024). https://doi.org/10.1007/s10278-024-00972-8

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  • DOI: https://doi.org/10.1007/s10278-024-00972-8

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