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

Journal of Medical and Biological Engineering (2022)Cite this article

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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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Funding

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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Author notes

  1. Co-first author: Yishu Deng and Dan Hou.

Authors and Affiliations

  1. State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Collaborative Innovation Center for Cancer Medicine, Information Technology Center, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Yishu Deng, Bin Li, Bingzhong Jing & Chaofeng Li

  2. School of Medicine, Tsinghua University, Beijing, 100084, People’s Republic of China

    Dan Hou

  3. Guangzhou Deepaint Intelligence Tenchnology Co.Ltd, Guangzhou, 510000, People’s Republic of China

    Dan Hou

  4. Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Xing Lv & Mengyun Qiang

  5. Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Liangru Ke

  6. Shenzhen Annet Information System Co.LTD, Guangzhou, 518000, People’s Republic of China

    Taihe Li

Authors
  1. Yishu Deng
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  2. Dan Hou
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  3. Bin Li
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  4. Xing Lv
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  7. Taihe Li
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  8. Bingzhong Jing
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  9. Chaofeng Li
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Corresponding authors

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). https://doi.org/10.1007/s40846-022-00710-x

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  • DOI: https://doi.org/10.1007/s40846-022-00710-x

Keywords

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