A k-Dense-UNet for Biomedical Image Segmentation

  • Zhiwen Qiang
  • Shikui TuEmail author
  • Lei XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)


Medical image segmentation is the premise of many medical image applications including disease diagnosis, anatomy, and radiation therapy. This paper presents a k-Dense-UNet for segmentation of Electron Microscopy (EM) images. Firstly, based on the characteristics of the long skip connection of U-Net and the mechanism of short skip connection of DenseNet, we propose a Dense-UNet by embedding the dense blocks into U-Net, leading to deeper layers for better feature extraction. We experimentally show that Dense-UNet outperforms the popular U-Net. Secondly, we combine Dense-UNet with one of the newest U-Net variants called kU-Net into a network called k-Dense-UNet, which consists of multiple Dense-UNet submodules. Skip connections are added between the adjacent submodules, to pass information efficiently, helping the model to identify fine features. Experimental results on the ISBI 2012 EM dataset show that k-Dense-UNet achieves better performance than U-Net and some of its variants.


Image segmentation Electron Microscopy Skip connection 



The first author would like to thank Yuze Guo for helpful discussions. This work was supported by the Zhi-Yuan Chair Professorship Start-up Grant (WF220103010), and Startup Fund (WF220403029) for Youngman Research, from Shanghai Jiao Tong University.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Centre for Cognitive Machines and Computational Health (CMaCH), SEIEE SchoolShanghai Jiao Tong UniversityShanghaiChina

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