Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting

Xiangrong Zhou, Takuya Kojima, Song Wang, Xinxin Zhou, Takeshi Hara, Taiki Nozaki, Masaki Matsusako, Hiroshi Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


We propose an automatic approach to anatomy partitioning on three-dimensional (3D) computed tomography (CT) images that divides the human torso into several volumes of interest (VOIs) according to anatomical definition. In the proposed approach, a deep convolutional neural network (CNN) is trained to automatically detect the bounding boxes of organs on two-dimensional (2D) sections of CT images. The coordinates of those boxes are then grouped so that a vote on a 3D VOI (called localization) for each organ can be obtained separately. We applied this approach to localize the 3D VOIs of 17 types of organs in the human torso and then evaluated the performance of the approach by conducting a four-fold crossvalidation using a dataset consisting of 240 3D CT scans with the human-annotated ground truth for each organ region. The preliminary results showed that 86.7% of the 3D VOIs of the 3177 organs in the 240 test CT images were localized with acceptable accuracy (mean of Jaccard indexes was 72.8%) compared to that of the human annotations. This performance was better than that of the state-of-the-art method reported recently. The experimental results demonstrated that using a deep CNN for anatomy partitioning on 3D CT images was more efficient and useful compared to the method used in our previous work.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
ISBN (Electronic)9781510625471
Publication statusPublished - 2019
Externally publishedYes
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: 2019 Feb 172019 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego


  • 3D CT images
  • Anatomical structures
  • Convolutional neural network
  • Deep learning
  • Organ localization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


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