Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (12): 1900-1909.doi: 10.23940/ijpe.20.12.p6.19001909

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Automatic Liver Segmentation Method based on Deep Learning and Region Growing Algorithm

Yongquan Xia*, Sihai Qiao, Qianqian Ye   

  1. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address:
  • Supported by:
    This work is supported by the National Natural Science Foundation of China No.81501547 and the Henan Province Science and Technology Research Project No.172102410080.

Abstract: Accurate medical image segmentation can assist doctors in disease diagnosis. It is very important to segment the liver accurately from medical images in the field of the liver. However, the low contrast of tissues and organs and uneven distribution of CT values in abdominal CT images makes liver segmentation difficult. In this paper, we propose a method of combining the improved U-Net network model and the region growing algorithm. The feature information of the pooling layer is directly extracted after two convolution and ReLU functions. The up-sample layer copies the feature information of the corresponding down-sampling layer. Softmax Layer calculates the amount of information loss to reduce the loss of feature information. Finally, the region growing algorithm is used to optimize the initial results. Five parameters of medical image segmentation are used for evaluation. DICE can reach more than 95.0% accuracy and other parameters have been increased accordingly. Experimental results show this method can accurately segment the liver area, solve the problems of blurred edges and unclear areas, and provide an effective basis for the diagnosis of liver disease.

Key words: s: deep learning, fully convolutional neural network, liver segmentation, regional growth, edge optimization