Empirical comparisons of deep learning networks on liver segmentation

Yi Shen, Victor S. Sheng, Lei Wang, Jie Duan, Xuefeng Xi, Dengyong Zhang, Ziming Cui

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver segmentation. Since original Resnet and Densenet could not perform image segmentation directly, we make some adjustments for them to perform live segmentation. Our experimental results show that Densenet performs the best on liver segmentation, followed by Resnet. Both perform much better than Segnet, U-Net, and FCN. Among Segnet, U-Net, and FCN, U-Net performs the best, followed by Segnet. FCN performs the worst.

Original languageEnglish
Pages (from-to)1233-1247
Number of pages15
JournalComputers, Materials and Continua
Issue number3
StatePublished - 2020


  • Deep learning
  • Densenet
  • FCN
  • Liver segmentation
  • Resnet
  • Segnet
  • U-Net


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