3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

  • Qi DouEmail author
  • Hao Chen
  • Yueming Jin
  • Lequan Yu
  • Jing Qin
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.


Conditional Random Field Discrimination Capability Compute Tomography Volume Convolutional Layer Conditional Random Field Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work described in this paper was supported by the following grants from the Research Grants Council of the Hong Kong Special Administrative Region (Project no. CUHK 412513 and CUHK 14202514).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Qi Dou
    • 1
    Email author
  • Hao Chen
    • 1
  • Yueming Jin
    • 1
  • Lequan Yu
    • 1
  • Jing Qin
    • 2
  • Pheng-Ann Heng
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
  2. 2.School of Nursing, Centre for Smart HealthThe Hong Kong Polytechnic UniversityHong KongChina

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