Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Journal: Artificial intelligence in medicine
Published Date: Mar 27, 2017

Abstract

This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD. In the case of 3Dircadb, using the FCN, the mean ratios of the volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSSD) were 15.6±4.3%, 5.8±3.5%, 2.0±0.9%, 2.9±1.5mm, 7.1±6.2mm, respectively. For JDRD, using the MC-FCN, the mean ratios of VOE, RVD, ASD, RMSD, and MSSD were 8.1±4.5%, 1.7±1.0%, 1.5±0.7%, 2.0±1.2mm, 5.2±6.4mm, respectively. The test results demonstrate that the MC-FCN model provides greater accuracy and robustness than previous methods.

Authors

  • Changjian Sun
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Shuxu Guo
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Huimao Zhang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.). Electronic address: huimao@jlu.edu.cn.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Meimei Chen
    College of Communication Engineering, Jilin University, Changchun, China.
  • Shuzhi Ma
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Lanyi Jin
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Xiaoming Liu
    College of Agriculture, Northeast Agricultural University, Harbin, China.
  • Xueyan Li
    College of Electronic Science and Engineering, Jilin University, Changchun, China. Electronic address: leexy@jlu.edu.cn.
  • Xiaohua Qian
    Department of Diagnostic Radiology, Wake Forest Medical School, Winston-Salem, NC 27103, USA.

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