title: MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images.
abstract: OBJECTIVE
  Automatic osteosarcoma tumor segmentation on computed tomography (CT) images is a challenging problem, as tumors have large spatial and structural variabilities. In this study, an automatic tumor segmentation method, which was based on a fully convolutional networks with multiple supervised side output layers (MSFCN), was presented.
  
  
  METHODS
  Image normalization is applied as a pre-processing step for decreasing the differences among images. In the frame of the fully convolutional networks, supervised side output layers were added to three layers in order to guide the multi-scale feature learning as a contracting structure, which was then able to capture both the local and global image features. Multiple feature channels were used in the up-sampling portion to capture more context information, for the assurance of accurate segmentation of the tumor, with low contrast around the soft tissue. The results of all the side outputs were fused to determine the final boundaries of the tumors.
  
  
  RESULTS
  A quantitative comparison of the 405 osteosarcoma manual segmentation results from the CT images showed that the average Dice similarity coefficient (DSC), average sensitivity, average Hammoude distance (HM) and F1-measure were 87.80%, 86.88%, 19.81% and 0.908, respectively. It was determined that, when compared with the other learning-based algorithms (for example, the fully convolution networks (FCN), U-Net method, and holistically-nested edge detection (HED) method), the MSFCN had the best performances in terms of DSC, sensitivity, HM and F1-measure.
  
  
  CONCLUSIONS
  The results indicated that the proposed algorithm contributed to the fast and accurate delineation of tumor boundaries, which could potentially assist doctors in making more precise treatment plans.
authors.affiliation: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China; University of Science and Technology of China, Hefei, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Huang
authors.affiliation: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Xia
authors.affiliation: Second Affiliated Hospital of Soochow University, Suzhou, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: University of Science and Technology of China, Hefei, China.
authors.forename: Bensheng
authors.initials: B
authors.lastname: Qiu
authors.affiliation: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. Electronic address: xingaosam@yahoo.com.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001859:Bone Neoplasms / Q000000981:diagnostic imaging*; D002648:Child; D003287:Contrast Media; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D015588:Observer Variation; D012516:Osteosarcoma / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015898:Tomography Scanners, X-Ray Computed; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult
chemical_list: D003287:Contrast Media
keywords: Convolutional neural networks; Multiple supervised networks; Osteosarcoma segmentation