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Radiological Physics and Technology

, Volume 7, Issue 2, pp 277–283 | Cite as

Development and evaluation of statistical shape modeling for principal inner organs on torso CT images

  • Xiangrong ZhouEmail author
  • Rui Xu
  • Takeshi Hara
  • Yasushi Hirano
  • Ryujiro Yokoyama
  • Masayuki Kanematsu
  • Hiroaki Hoshi
  • Shoji Kido
  • Hiroshi Fujita
Article

Abstract

The shapes of the inner organs are important information for medical image analysis. Statistical shape modeling provides a way of quantifying and measuring shape variations of the inner organs in different patients. In this study, we developed a universal scheme that can be used for building the statistical shape models for different inner organs efficiently. This scheme combines the traditional point distribution modeling with a group-wise optimization method based on a measure called minimum description length to provide a practical means for 3D organ shape modeling. In experiments, the proposed scheme was applied to the building of five statistical shape models for hearts, livers, spleens, and right and left kidneys by use of 50 cases of 3D torso CT images. The performance of these models was evaluated by three measures: model compactness, model generalization, and model specificity. The experimental results showed that the constructed shape models have good “compactness” and satisfied the “generalization” performance for different organ shape representations; however, the “specificity” of these models should be improved in the future.

Keywords

CT images 3D organ shapes Statistical shape model Point distribution model Minimum description length 

Notes

Acknowledgments

The authors thank members of the Fujita Laboratory. This research work was funded in part by a Grant-in-Aid for Scientific Research on Innovative Areas, and in part by a Grant-in-Aid for Scientific Research, MEXT, Japan.

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2014

Authors and Affiliations

  • Xiangrong Zhou
    • 1
    Email author
  • Rui Xu
    • 2
  • Takeshi Hara
    • 1
  • Yasushi Hirano
    • 2
  • Ryujiro Yokoyama
    • 1
  • Masayuki Kanematsu
    • 3
  • Hiroaki Hoshi
    • 4
  • Shoji Kido
    • 2
  • Hiroshi Fujita
    • 1
  1. 1.Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of MedicineGifu UniversityGifuJapan
  2. 2.Graduate School of MedicineYamaguchi UniversityUbeJapan
  3. 3.Department of RadiologyGifu University HospitalGifuJapan
  4. 4.Department of Radiology, Division of Tumor Control, Graduate School of MedicineGifu UniversityGifuJapan

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