Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (11): 18-22.doi: 10.16180/j.cnki.issn1007-7820.2019.11.004

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Gland Cell Image Segmentation Algorithm Based on Improved U-Net Network

BEI Chenyuan1,YU Haibin1,PAN Mian1,JIANG Jie1,LÜ Bingyun2   

  1. 1. School of Electronic and Information,Hangzhou Dianzi University,Hangzhou 310018,China
    2. Zhejiang Dahua Technology Co. Ltd.,Hangzhou 310053,China
  • Received:2018-11-01 Online:2019-11-15 Published:2019-11-15
  • Supported by:
    Natural Science Foundation of Zhejiang Province(LY18F010014)


This paper proposed a full convolutional neural network model with attention module to solve the problem that multi-scale targets and information loss affect the segmentation accuracy of gland images in the automatic segmentation process. This model followed the encoder-decoder structure. Firstly, the atrous spatial pyramid pooling was added to the encoder path, and the original residual convolution layer was replaced by the atrous residual convolution layer in the encoder path. Secondly, the attention module was added to the decoder path to make the model output high-resolution feature map and improve the segmentation accuracy of the multi-scale object. The experimental results showed that the proposed network model had fewer parameters, high segmentation precision and good robustness, besides, the average segmentation accuracy of gland images was as high as 89.7%.

Key words: full convolutional neural network, encoder-decoder structure, atrous spatial pyramid pooling, attention module, high-resolution feature map, high segmentation precision

CLC Number: 

  • TP391