title: Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.
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.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China.
authors.forename: Changjian
authors.initials: C
authors.lastname: Sun
authors.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China.
authors.forename: Shuxu
authors.initials: S
authors.lastname: Guo
authors.affiliation: Radiology, The First Hospital of Jilin University, Changchun, China.
authors.forename: Huimao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Radiology, The First Hospital of Jilin University, Changchun, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Communication Engineering, Jilin University, Changchun, China.
authors.forename: Meimei
authors.initials: M
authors.lastname: Chen
authors.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China.
authors.forename: Shuzhi
authors.initials: S
authors.lastname: Ma
authors.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China.
authors.forename: Lanyi
authors.initials: L
authors.lastname: Jin
authors.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
authors.affiliation: College of Electronic Science and Engineering, Jilin University, Changchun, China. Electronic address: leexy@jlu.edu.cn.
authors.forename: Xueyan
authors.initials: X
authors.lastname: Li
authors.affiliation: Radiology, Wake Forest School of Medicine, Winston Salem, USA. Electronic address: qianxh1982@gmail.com.
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Qian
mesh_terms: D001185:Artificial Intelligence; D001331:Automation; D003287:Contrast Media / Q000008:administration & dosage*; D016208:Databases, Factual; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed
chemical_list: D003287:Contrast Media
keywords: Feature fusion; Fully convolutional networks; Multi-channel