Interactive Liver Segmentation in CT Volumes Using Fully Convolutional Networks

  • Titinunt Kitrungrotsakul
  • Yutaro Iwamoto
  • Xian-Hua Han
  • Xiong Wei
  • Lanfen Lin
  • Hongjie Hu
  • Huiyan Jiang
  • Yen-Wei ChenEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)


Organ segmentation is one of the most fundamental and challenging task in computer aided diagnosis (CAD) systems, and segmenting liver from 3D medical data becomes one of the hot research topics in medical analysis field. Graph cut algorithms have been successfully applied to medical image segmentation of different organs for 3D volume data which not only leads to very large-scale graph due to the same node number as voxel number. Slice by Slice liver segmentation method is one of the technique that normally used to solve the memory usage. However, the computation times are increased and reduce the accuracy. In this paper we propose an interactive organ segmentation using fully convolutional networks. The network will perform slice by slice which only 1 slice of seed points in each volume. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 20 CT volumes, focus on liver organ and most of which have tumors inside of the liver, and abnormal deformed shape of liver. Our method can segment with 0.95401 dice accuracy with better than stage-of-the-art methods.


Fully convolutional networks Interactive Segmentation Liver Seed points 



This work is supported in part by Japan Society for Promotion of Science (JSPS) under Grant No. 16J09596 and KAKEN under the Grant Nos. 16H01436, 17H00754, 17K00420, 18H03267; and in part by the MEXT Support Program for the Strategic Research Foundation at Private Universities, Grand No. S1311039 (2013–2017), and also partially supported by A*STAR Research Attachment Program.


  1. 1.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Lamecker, H., Lange, T., Seebass, M.: A statistical shape model for the liver. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 412–427 (2002)CrossRefGoogle Scholar
  3. 3.
    Dong, C., et al.: Segmentation of liver and spleen based on computational anatomy models. Comput. Biol. Med. 67, 146–160 (2015)CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary & region segmentation of object in N-D images. In: International Conference on Computer Vision, pp. 105–112 (2001)Google Scholar
  5. 5.
    Kitrungrotsakul, T., Han, X.-H., Chen, Y.-W.: Liver segmentation using superpixel-based graph cuts and regions of shape constraints. In: Proceedings of IEEE International Conference on Image Processing (ICIP2015), pp. 3368–3371 (2015)Google Scholar
  6. 6.
    Grady, L.: Random walks for image segmentation. IEEE Trans. PAMI 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  7. 7.
    Dong, C., et al.: Simultaneous segmentation of multiple organs using random walks. J. Inf. Process. Soc. Jpn. 24(2), 320–329 (2016)MathSciNetGoogle Scholar
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  9. 9.
    Long, J., Shelhamer, E.: Fully convolutional models for semantic segmentation. In: Proceedings of CVPR 2015 (2015)Google Scholar
  10. 10.
    Chung, F., Delingette, H.: Regional appearance modeling based on the clustering of intensity profiles. Comput. Vis. Image Underst. 117(6), 705–717 (2013)CrossRefGoogle Scholar
  11. 11.
    Erdt, M., Steger, S., Kirschner, M., Wesarg, S.: Fast automatic liver segmentation combining learned shape priors with observed shape deviation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 249–254 (2010)Google Scholar
  12. 12.
    Li, G., Chen, X., Shi, F., Zhu, W., Tian, J.: Automatic liver segmentation based on shape constraints and deformable graph cut in CT images. IEEE Trans. Image Process. 24(12), 5315–5329 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    IRCAD dataset. Accessed 30 Jan 2018

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Titinunt Kitrungrotsakul
    • 1
  • Yutaro Iwamoto
    • 1
  • Xian-Hua Han
    • 2
  • Xiong Wei
    • 3
  • Lanfen Lin
    • 4
  • Hongjie Hu
    • 5
  • Huiyan Jiang
    • 6
  • Yen-Wei Chen
    • 1
    • 4
    Email author
  1. 1.Graduate School of Information Science and EngineeringRitsumeikan UniversityKyotoJapan
  2. 2.Faculty of ScienceYamaguchi UniversityYamaguchiJapan
  3. 3.Institute for Infocomm ResearchSingaporeSingapore
  4. 4.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  5. 5.Radiology Department, Sir Run Run Shaw Hospital, Medical SchoolZhejiang UniversityHangzhouChina
  6. 6.Software CollegeNortheastern UniversityShenyangChina

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