Liver tumor segmentation in CT volumes using an adversarial densely connected network.

Journal: BMC bioinformatics
Published Date: Dec 2, 2019

Abstract

BACKGROUND: Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task.

Authors

  • Lei Chen
    Department of Chemistry, Stony Brook University Stony Brook NY USA.
  • Hong Song
    School of Software, Beijing Institute of Technology, Beijing, China.
  • Chi Wang
    School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China.
  • Yutao Cui
    School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China.
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
  • Xiaohua Hu
    Department of Information, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.

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