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Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation

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Abstract

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters.

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Data availability

The datasets used in this paper are public datasets. The IOSTAR and RC-SLO for this study can be found at https://www.retinacheck.org.

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Funding

This research was funded by Project supported by the Education Department of Hainan Province, (Grant No. Hnjg2021ZD-10).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Peipei Li, Zhao Qiu & Sheng Yuan

  2. Affiliated maternal and child health hospital (Children’s hospital) of Hainan medical university/Hainan Women and Children’s Medical Center, Haikou, 570312, China

    Yuefu Zhan

  3. Hainan Provincial Public Security Department, Haikou, 570203, China

    Huajing Chen

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  1. Peipei Li
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Contributions

Conceptualization: Peipei Li; Methodology: Peipei Li; Formal analysis and investigation: Peipei Li; Writing - original draft preparation: Peipei Li, Zhao Qiu, Yuefu Zhan; Writing - review and editing: Zhao Qiu, Yuefu Zhan, Huajing Chen, Sheng Yuan; Funding acquisition: Zhao Qiu; Resources: Zhao Qiu; Supervision: Zhao Qiu. All authors contributed to the article and approved the submitted version.

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Correspondence to Zhao Qiu or Yuefu Zhan.

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Li, P., Qiu, Z., Zhan, Y. et al. Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation. J Med Syst 47, 102 (2023). https://doi.org/10.1007/s10916-023-01992-7

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