Computer Science ›› 2020, Vol. 47 ›› Issue (5): 166-171.doi: 10.11896/jsjkx.190400062

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Retinal Vessel Segmentation Based on Dual Attention and Encoder-decoder Structure

LI Tian-pei, CHEN Li   

  1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China
    Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan University of Science and Technology,Wuhan 430065,China
  • Received:2019-04-09 Online:2020-05-15 Published:2020-05-19
  • About author:LI Tian-pei,born in 1995,postgraduate.His main research interests include ima-ge processing and deep learning.
    CHEN Li,born in 1977,Ph.D,professor,Ph.D supervisor.His main research interests include image processing,computer vision,intelligent media computing,and deep learning applications.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61773297) and Open Fund of Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System(2016znss01A)

Abstract: The segmentation of the retinal vessels in fundus image is important for the diagnosis of ophthalmic diseases such as diabetes,retinopathy and glaucoma.Aiming at the difficulties of extracting blood vessels from retinal blood vessel images and the lack of data samples,a retinal vessel segmentation method combining attention module with encoder-decoder structure is proposed.To improve the segmentation effect of retinal blood vessels,a spatial and channel attention module is added to each convolutional layer of the encoder-decoder convolutional neural network to enhance the utilization of the spatial and channel information of the image features (such as the size,shape,and connectivity of the blood vessels),where the spatial attention focuses on the topological characteristics of blood vessels,and the channel attention focuses on the correct classification of blood vessel pixels.Moreover,the Dice loss function is used to solve the imbalance of positive and negative samples in retinal blood vessel images.The proposed method has been applied on three public fundus image databases DRIVE,STARE and CHASE_DB1.The experimental data show that the accuracy,sensitivity,specificity and AUC values are superior to the existing retinal vessel segmentation me-thods,with AUC values of 0.9889,0.9812 and 0.9831,respectively.The experimental results show that the proposed method can effectively extract the vascular network in healthy retinal images and diseased retinal images,and can segment small blood vessels well.

Key words: Channel attention, Encoder decoder structure, Feature of proposed method visualization, Segmentation of retinal blood vessels, Spatial attention

CLC Number: 

  • TP391
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