Automatic Extraction of Liver Region from Medical Images by Using an MFUnet

  • Vi, Vo Thi Tuong (Dept. of AI Convergence, Chonnam National University) ;
  • Oh, A-Ran (Dept. of AI Convergence, Chonnam National University) ;
  • Lee, Guee-Sang (Dept. of AI Convergence, Chonnam National University) ;
  • Yang, Hyung-Jeong (Dept. of AI Convergence, Chonnam National University) ;
  • Kim, Soo-Hyung (Dept. of AI Convergence, Chonnam National University)
  • Received : 2020.02.13
  • Accepted : 2020.09.15
  • Published : 2020.09.30


This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.


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