Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

  • Lijun Zhao
  • Zixiao Lu
  • Jun Jiang
  • Yujia Zhou
  • Yi Wu
  • Qianjin FengEmail author


Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography–computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.


Nasopharyngeal carcinoma Segmentation PET-CT Fully convolutional neural networks 


Funding information

This work was supported by the National Natural Science Foundation Joint Fund Key Support Project under Grant U1501256 and the Applied Science and Technology Research and Development Special Project in Guangdong Province (No. 2015B010131011).


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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  • Lijun Zhao
    • 1
  • Zixiao Lu
    • 1
  • Jun Jiang
    • 1
  • Yujia Zhou
    • 1
  • Yi Wu
    • 1
  • Qianjin Feng
    • 1
    Email author
  1. 1.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina

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