Cong-Zhong Wu, Jun Sun, Jing Wang, Liang-Feng Xu, Shu Zhan. Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation. International Journal of Automation and Computing.
Citation: Cong-Zhong Wu, Jun Sun, Jing Wang, Liang-Feng Xu, Shu Zhan.

Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation

. International Journal of Automation and Computing.

Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation

doi: 10.1007/s11633-020-1277-0
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  • Author Bio:

    Cong-Zhong Wu received the M. Sc. degree in microwave specialty from Institute of Plasma Physics, Chinese Academy of Sciences, China in 1990. He is now an associate professor of School of Computer and Information Engineering, Hefei University of Technology, China. His research interests include wireless communication, DSP technology and application, embedded application system development. E-mail: 17368836384@163.comORCID iD: 0000-0001-7261-5053

    Jun Sun received the B.Sc. degree in electronic information engineering from Jiangsu University, China in 2018. Now, he is a graduate student in Hefei University of technology, China. His research interests include deep learning and computer vision. E-mail: iD: 0000-0002-5568-496X

    Jing Wang received the M. D. degree in ophthalmic optometry from Anhui Medical University, China in 2008. She is now an attending physician in the Second Affiliated Hospital of Anhui Medical University, China. Her research interests include second laser treatment of myopia, high myopia comprehensive treatment, myopia and hyperopia correction.E-mail:

    Liang-Feng Xu received the B. Sc. degree in electronic information engineering from Hefei University of technology, China in 1993, and received the M.Eng. degree in signal and information processing from School of Computer and Information Technology, Hefei University of Technology, China in 2003. Since 1993, he has been engaged in teaching and scientific research in School of Computer and Information, Hefei University of Technology, China. Her research interests include wireless communication, image processing and facial expression recognition. E-mail:

    Shu Zhan received Ph.D. degree in information and communication system from University of Science and Technology of China, China in 2000. He has been a postdoctoral researcher in School of Information, Tokyo University of Japan from 2002 to 2004. He has been a professor in School of Computer Science and Information Engineering, Hefei University of Technology, China since 1993. His research interests include digital image analysis, medical image analysis and pattern recognition. E-mail: (Corresponding author) ORCID iD: 0000-0002-5568-0264

  • Received Date: 2020-09-20
  • Accepted Date: 2021-01-06
  • Available Online: 2021-03-01
  • Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks (CNNs), many tasks can be solved by CNNs. In this paper, we propose an encoding-decoding network with a pyramid self-attention module (PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks (FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE_DB1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve (AUC) are 82.21%/80.57%, 95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE_DB1, respectively.


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