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Retinal Vessel Segmentation Using Multi-scale Generative Adversarial Network with Class Activation Mapping

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Wireless Mobile Communication and Healthcare (MobiHealth 2021)

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Abstract

Retinal vessel segmentation plays a significant role in the accurate diagnosis of retinal diseases. However, existing methods commonly omit micro-vessels in retinal images and generate some false-positive vessels. To alleviate this issue, we propose a multi-scale generative adversarial network with class activation mapping to achieve efficient segmentation. For the problem of small amount of data, we introduce a novel data augmentation method, which can generate multiple samples by cutting pixels from other samples. This method increases the diversity of samples and improve the robustness of the model. We compare our method with previous models with several metrics, and experiments show the superiority and effectiveness of our model.

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Acknowledgement

This work was supported in part by the National Key Research and Development Program of China (Grant No. 2019YFA0706200), in part by the National Natural Science Foundation of China (Grant No. 61632014, No. 61627808, No. 61802159), in part by Fundamental Research Funds for Central Universities (lzujbky-2019-26).

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Authors and Affiliations

  1. Lanzhou University, Lanzhou, 730099, Gansu, China

    Minqiang Yang, Yinru Ye, Kai Ye, Xiping Hu & Bin Hu

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  1. Minqiang Yang
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  2. Yinru Ye
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  3. Kai Ye
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  4. Xiping Hu
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  5. Bin Hu
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Correspondence to Xiping Hu or Bin Hu .

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Editors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  2. The University of Sydney, Sydney, NSW, Australia

    Abbas Jamalipour

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Guo

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Yang, M., Ye, Y., Ye, K., Hu, X., Hu, B. (2022). Retinal Vessel Segmentation Using Multi-scale Generative Adversarial Network with Class Activation Mapping. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_7

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  • DOI: https://doi.org/10.1007/978-3-031-06368-8_7

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