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Retinal vessel segmentation method based on RSP-SA Unet network

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Abstract

Segmenting retinal vessels plays a significant role in the diagnosis of fundus disorders. However, there are two problems in the retinal vessel segmentation methods. First, fine-grained features of fine blood vessels are difficult to be extracted. Second, it is easy to lose track of the details of blood vessel edges. To solve the problems above, the Residual SimAM Pyramid-Spatial Attention Unet (RSP-SA Unet) is proposed, in which the encoding, decoding, and upsampling layers of the Unet are mainly improved. Firstly, the RSP structure proposed in this paper approximates a residual structure combined with SimAM and Pyramid Segmentation Attention (PSA), which is applied to the encoding and decoding parts to extract multi-scale spatial information and important features across dimensions at a finer level. Secondly, the spatial attention (SA) is used in the upsampling layer to perform multi-attention mapping on the input feature map, which could enhance the segmentation effect of small blood vessels with low contrast. Finally, the RSP-SA Unet is verified on the CHASE_DB1, DRIVE, and STARE datasets, and the segmentation accuracy (ACC) of the RSP-SA Unet could reach 0.9763, 0.9704, and 0.9724, respectively. Area under the ROC curve (AUC) could reach 0.9896, 0.9858, and 0.9906, respectively. The RSP-SA Unet overall performance is better than the comparison methods.

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Funding

The authors acknowledge the support from the National Natural Science Foundation of China under Grant No. 62205091, the Postdoctoral Science Foundation of China under Grant No. 2022M710983, the Heilongjiang Provincial Postdoctoral Foundation Grant No. LBH-Z22201, and the Fundamental Research Foundation for Universities of Heilongjiang Province under Grant No. 2022-KYYWF-0121.

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Authors and Affiliations

  1. The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China

    Kun Sun, Yang Chen, Fuxuan Dong, Qing Wu, Jiameng Geng & Yinsheng Chen

  2. Teaching Demonstration Center for Measurement and Control Technology and Instrumentation, National Experimental, Harbin University of Science and Technology, Harbin, China

    Kun Sun, Yang Chen, Fuxuan Dong, Qing Wu, Jiameng Geng & Yinsheng Chen

  3. Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin, China

    Qing Wu

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  1. Kun Sun
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Correspondence to Qing Wu.

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Sun, K., Chen, Y., Dong, F. et al. Retinal vessel segmentation method based on RSP-SA Unet network. Med Biol Eng Comput 62, 605–620 (2024). https://doi.org/10.1007/s11517-023-02960-6

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