Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

Journal: Medical physics
Published Date: Aug 31, 2017

Abstract

PURPOSE: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image.

Authors

  • Xiangrong Zhou
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1194, Japan.
  • Ryosuke Takayama
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, Gifu, 501-1194, Japan.
  • Song Wang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Takeshi Hara
    Department of Psychosomatic Medicine, Endocrinology and Diabetes Mellitus, Fukuoka Tokushukai Hospital, Kasuga, Fukuoka, Japan.
  • Hiroshi Fujita
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

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