Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (3): 38-44.doi: 10.16180/j.cnki.issn1007-7820.2022.03.006

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CTA Segmentation Algorithm of Abdominal Artery Based on 3D Fully Convolutional Network

Lingyu JI,Yongbin GAO,Chenglu ZHAO,Xianhua TANG,Kaicheng XU,Jiacheng XU   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Online:2022-03-15 Published:2022-04-02
  • Supported by:
    Fund: National Natural Science Foundation of China(61802253);Key Project of Shanghai Science and Technology Commission(18411952800)


Convolutional neural networks have become a research hotspot in the field of abdominal artery segmentation. The classic convolutional network has the problems of low segmentation accuracy and discontinuous segmentation of blood vessels. In view of these problems, this study proposes an abdominal arterial vessel segmentation algorithm based on an improved 3D full convolutional network. The side input of different scales is constructed on the encoding path of the network, and the convoluted image of side input is fused with the convoluted image of down sampling to extract more feature information. Meanwhile, a new multi-scale feature extraction module is embedded in the network. In this module, the channel attention and dense dilation convolution are introduced to capture the higher-level feature information. The experimental results on abdominal artery segmentation show that compared with other segmentation methods, the proposed method is more intuitive and quantitative, indicating that this method can improve the accuracy of blood vessel segmentation.

Key words: medical image processing, computed tomography, abdominal vascular segmentation, 3D convolution neural network, dilation convolution, channel attention mechanism, multi-scale feature fusion

CLC Number: 

  • TP391