A Bypass-Based U-Net for Medical Image Segmentation

  • Kaixuan Chen
  • Gengxin Xu
  • Jiaying Qian
  • Chuan-Xian RenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)


U-Net has been one of the important deep learning models applied for biomedical image segmentation for a few years. In this paper, inspired by the way how fully convolutional network (FCN) makes dense predictions, we modify U-Net by adding a new bypass for the expansive path. Before combining the contracting path with the upsampled output, we connect with the feature maps from a deeper encoding convolutional layer for the decoding up-convolutional units, and sum up the information learned from both sides. Also, we have implemented this modification to recurrent residual convolutional neural network based on U-Net as well. The experimental results show that the proposed bypass-based U-Net can gain further context information, especially the details from the previous convolutional layer, and outperforms the original U-Net on the DRIVE dataset for retinal vessel segmentation and the ISBI 2018 challenge for skin lesion segmentation.


U-Net Medical image segmentation Retinal vessel segmentation Skin lesion segmentation 



This work is supported in part by the National Natural Science Foundation of China under Grant 61976229, 61906046, 61572536, 11631015, U1611265 and in part by the Science and Technology Program of Guangzhou under Grant 201804010248.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kaixuan Chen
    • 1
  • Gengxin Xu
    • 1
  • Jiaying Qian
    • 1
  • Chuan-Xian Ren
    • 1
    Email author
  1. 1.School of MathematicsSun Yat-sen UniversityGuangzhouPeople’s Republic of China

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