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A Novel Fully Automated MRI-Based Deep-Learning Method for Segmentation of Nasopharyngeal Carcinoma Lymph Nodes



Nasopharyngeal carcinoma (NPC) is epidemic in south China, especially in Guangdong province. Radiotherapy is the main treatment, with the non-keratinizing type accounting for more than 95% of cases. Metastatic lymph nodes, which should be included in the radiotherapy target volume, are detected among approximately 70–80% of cases when the disease is first diagnosed. Accurate spatial modelling of metastatic lymph nodes is important for successful treatment.


We propose a coarse-to-fine deep supervision convolutional neural network (CF-Net) to perform metastatic lymph node segmentation using a 3D residual V-Net. Contrast-enhanced axial T1-weighted (T1C) magnetic resonance images of more than 6000 patients with NPC were enrolled in this study. We used the probability map predicted at a coarse scale as the weight map for training at a fine scale. This method draws attention to a fine scale within an area already detected at a coarse scale.


CF-Net achieves a median Dice score of 81.0% in the segmentation of metastatic lymph nodes with a sensitivity and specificity of 79.1% and 99.2%, respectively.


The results show that our method can accurately identify, locate and segment NPC lymph nodes. We compared CF-Net with popular methods: V-Net, DeepLab-v3, HR-Net, and DenseNet. Our proposed method, across all variants, consistently and statistically outperformed the other models.

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  1. Torre, L., Bray, F., Siegel, R., Ferlay, J., Lortet-Tieulent, J., & Jemal, A. (2015). Global cancer statistics, 2012. CA: A Cancer Journal for Clinicians, 65(2), 87–108.

    Google Scholar 

  2. Chen, Y., Sun, Y., Zong, J., Li, W., Chen, M., Chen, L., Mao, Y., Tang, L., Guo, Y., Lin, A., Liu, M., & Ma, J. (2013). Progress report of a randomized trial comparing long-term survival and late toxicity of concurrent chemoradiotherapy. Cancer, 119(12), 2230–2238.

    CAS  Article  Google Scholar 

  3. Head and Neck Cancers, National Comprehensive Cancer Network, (2018)

  4. Lai, S., Li, W., Chen, L., Luo, W., Chen, Y., Liu, L., Sun, Y., Lin, A., Liu, M., & Ma, J. (2011). How does intensity-modulated radiotherapy versus conventional two-dimensional radiotherapy influence the treatment results in nasopharyngeal carcinoma patients? International Journal of Radiation Oncology Biology Physics, 80(3), 661–668.

    Article  Google Scholar 

  5. Yi, J., Gao, L., Huang, X., Li, S., Luo, J., Cai, W., Xiao, J., & Xu, G. (2006). Nasopharyngeal carcinoma treated by radical radiotherapy alone: Ten-year experience of a single institution. International Journal of Radiation Oncology Biology Physics, 65(1), 161–168.

    Article  Google Scholar 

  6. Chen, L., Hu, C., Chen, X., Hu, G., Cheng, Z., Sun, Y., Li, W., Chen, Y., Xie, F., Liang, S., Chen, Y., Xu, T., Li, B., Long, G., Wang, S., Zheng, B., Guo, Y., Sun, Y., Mao, Y., … Ma, J. (2012). Concurrent chemoradiotherapy plus adjuvant chemotherapy versus concurrent chemoradiotherapy alone in patients with locoregionally advanced nasopharyngeal carcinoma: A phase 3 multicentre randomised controlled trial. Lancet Oncology, 13(2), 163–171.

    Article  Google Scholar 

  7. Blanchard, P., Lee, A., Marguet, S., Leclercq, J., Ng, W., Ma, J., Chan, A., Huang, P., Benhamou, E., Zhu, G., Chuan, D., Chen, Y., Mai, H., Kwong, D., Cheah, S., Moon, J., Tung, Y., Chi, K., Fountzila, G., … MAC-NPC Collaborative Group. (2015). Chemotherapy and radiotherapy in nasopharyngeal carcinoma: an update of the MAC-NPC meta-analysis. Lancet Oncology, 16(6), 645–655.

    Article  Google Scholar 

  8. Chong, V., Fan, Y., & Khoo, J. (1995). Retropharyngeal lymphadenopathy in nasopharyngeal carcinoma. European Journal of Radiology, 21(2), 100–105.

    CAS  Article  Google Scholar 

  9. Chong, V., & Ong, C. (2008). Nasopharyngeal carcinoma. European Journal of Radiology, 66(3), 437–447.

    CAS  Article  Google Scholar 

  10. Medical Segmentation Decathlon competition, [Online].

  11. Isensee, F., Petersen, J., Kohl, S., Jager, P., Haier-Hein, K. (2019). nnU-Net: breaking the spell on successful medical image segmentation

  12. Milletari, F., Navab, N., Ahmadi, S.-A. (2016). V-Net: fully convolutional neural networks for volumetric medical image segmentation

  13. Chen, L.-C., Papandreou, G., Kokkinos, L., Murphy, K., & Luille, A. (2018). DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.

    Article  Google Scholar 

  14. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation

  15. Sun, K., Xiao, B., Liu, D., Wang, J. (2019). Deep high-resolution representation learning for human pose estimation. Conference on Computer Vision and Pattern Recognition

  16. Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., Xiao, B. (2019) Deep high-resolution representation learning for visual recognition

  17. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y. (2017) The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

  18. Huang, G., Liu, Z., Matten, L., Weinberger, K. (2017) Densely connected convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition

  19. Rommeberger, O., Fischer, P., Brox, T. (2015) U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention

  20. He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition.

  21. He, K., Zhang, X., Ren, S., Sun, J. (2016). Identity mappings in deep residual networks. In European Conference on Computer Vision

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This work was supported by the National Natural Science Foundation of China under [Grant No. 82172862, 81702873], the Fundamental Research Funds for the Central Universities under Grant No. 19tkpy201. This study was approved by the Research Ethics Committee of Sun Yat-sen University Cancer Center (SYSUCC), and written informed consent was obtained from all patients before treatment. The key raw data have been uploaded onto the Research Data Deposit public platform (RDD), with the approval of RDD number RDDB2019000564.

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Correspondence to Bingzhong Jing or Chaofeng Li.

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Deng, Y., Hou, D., Li, B. et al. A Novel Fully Automated MRI-Based Deep-Learning Method for Segmentation of Nasopharyngeal Carcinoma Lymph Nodes. J. Med. Biol. Eng. (2022).

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  • Convolutional neural networks
  • Coarse-to-fine
  • Metastatic lymph
  • Nasopharyngeal carcinoma
  • Segmentation