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Segmentation of Lymph Nodes in Ultrasound Images Using U-Net Convolutional Neural Networks and Gabor-Based Anisotropic Diffusion

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Abstract

Purpose

The automated segmentation of lymph nodes (LNs) in ultrasound images is challenging, largely because of speckle noise and echogenic hila. This paper proposes a fully automatic and accurate method for LN segmentation in ultrasound that overcomes these issues.

Methods

The proposed segmentation method integrates diffusion-based despeckling, U-Net convolutional neural networks and morphological operations. First, the speckle noise is suppressed and the lymph node edges are enhanced using Gabor-based anisotropic diffusion (GAD). Then, a modified U-Net model is used to segment the LNs excluding any echogenic hila. Finally, morphological operations are undertaken to segment the entire LNs by filling in any regions occupied by echogenic hila.

Results

A total of 531 lymph nodes from 526 patients were segmented using the proposed method. Its segmentation performance was evaluated in terms of its accuracy, sensitivity, specificity, Jaccard similarity and Dice coefficient, for which it achieved values of 0.934, 0.939, 0.937, 0.763 and 0.865, respectively.

Conclusion

The proposed method automatically and accurately segments LNs in ultrasound images, enhancing the prospects of being able to undertake artificial intelligence (AI)-based diagnosis of lymph node diseases.

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Acknowledgements

The work was funded by the National Natural Science Foundation of China (Grant Nos. 62071285 and 61911530249).

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Author notes

  1. Haobo Chen and Yuqun Wang have contributed equally and are co-first authors.

Authors and Affiliations

  1. Shanghai Institute for Advanced Communication and Data Science, The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai University, Shanghai, China

    Haobo Chen, Jie Shi, Jingyu Xiong & Qi Zhang

  2. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Haobo Chen, Jie Shi, Jingyu Xiong & Qi Zhang

  3. Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 Xianxia Rd, Shanghai, 200050, China

    Yuqun Wang, Jianwei Jiang, Wanying Chang & Man Chen

  4. School of Communication and Information Engineering, Shanghai University, Nanchen Rd, Room 803, Xiangying Building, No. 333, Shanghai, 200444, China

    Qi Zhang

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  1. Haobo Chen
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  3. Jie Shi
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Correspondence to Man Chen or Qi Zhang.

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Chen, H., Wang, Y., Shi, J. et al. Segmentation of Lymph Nodes in Ultrasound Images Using U-Net Convolutional Neural Networks and Gabor-Based Anisotropic Diffusion. J. Med. Biol. Eng. 41, 942–952 (2021). https://doi.org/10.1007/s40846-021-00670-8

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  • DOI: https://doi.org/10.1007/s40846-021-00670-8

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