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

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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.

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References

  1. Frumin M, Golland P, Kikinis R, Hirayasu Y, Salisbury DF, Hennen J, Dickey CC, Anderson M, Jolesz FA, Grimson WE, McCarley RW, Shenton ME. Shape differences in the corpus callosum in first-episode schizophrenia and first-episode psychotic affective disorder. Am J Psychiatry. 2002;159:866–8.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Nappi J, Frimmel H, Yoshida H. Virtual endoscopic visualization of the colon by shape-scale signatures. Inf Technol Biomed IEEE Trans. 2005;9:120–31.

    Article  Google Scholar 

  3. Yoshida H, Nappi J, MacEneaney P, Rubin DT, Dachman AH. Computer-aided diagnosis scheme for detection of polyps at CT colonography. Radiographics. 2002;22:963–79.

    Article  PubMed  Google Scholar 

  4. Berks M, Caulkin S, Rahim R, Boggis C, Astley S. Statistical appearance models of mammographic masses. Proc IWDM. 2008;2008:401–8.

    Google Scholar 

  5. Heimann T, Meinzer H. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13:543–63.

    Article  PubMed  Google Scholar 

  6. Cootes T, Taylor C, Cooper D, Graham J. Active shape models—their training and application. Comput Vision Image Underst. 1995;61:38–59.

    Article  Google Scholar 

  7. Cootes T, Hill A, Taylor C, Haslam J. The use of active shape models for locating structures in medical images. Image Vis Comput. 1994;12:355–66.

    Article  Google Scholar 

  8. Yamaguchi S, Zhou X, Xu R, Hara T, Yokoyama R, Kanematsu M, Hoshi H, Kido S, Fujita H. Construction of statistical shape models of organs in torso CT scans using MDL method. Proc Int Forum Med Imaging Asia. 2012;2012:2–33.

    Google Scholar 

  9. Xu R, Zhou X, Hirano Y, Tachibana R, Hara T, Kido S, Fujita H. Evaluation of group-wise based methods for statistical shape models construction. Japanese Society of Medical Imaging Technology 2011. 2011; CD-ROM, OP1-7.

  10. Yamagichi S, Hayashi T, Zhou X, Hara T, Yokoyama R, Kanematsu M, Hoshi H, Fujita H. An interactive method for organ region segmentation in X-ray CT images, Japanese Society of Medical Imaging Technology 2011. 2011; CD-ROM, OP1-8.

  11. William EL, Harvey EC. Marching Cubes: a high resolution 3D surface construction algorithm. Comput Graph. 1987;21:163–9.

    Article  Google Scholar 

  12. The Visualization Toolkit (VTK). http://www.vtk.org. Accessed 25 Feb 2014.

  13. Davies R, Twining C, Cootes T. A minimum description length approach to statistical shape modeling. IEEE Trans Med Imaging. 2002;21:525–37.

    Article  PubMed  Google Scholar 

  14. Heimann T, Wolf I, Williams T, Meinzer H. 3D active shape models using gradient descent optimization of description length. Proc IPMI’05. 2005; 3565:566–77.

  15. Xu R, Zhou X, Hirano Y, Tachibana R, Hara T, Kido S, Fujita H. Particle-system based adaptive sampling on spherical parameter space to improve the MDL method for construction of statistical shape models. Comput Math Methods Med. 2013;2013:1–9, Article ID 196259.

    Google Scholar 

  16. Pizer SM, Fletcher PT, et al. Deformable m-reps for 3D medical image segmentation. Int J Comput Vision. 2003;55:85–106.

    Article  Google Scholar 

  17. Székely G, Kelemen A, Brechbühler C, Gerig G. Segmentation of 2-D and 3-D objects form MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models. Med Image Anal. 1996;1:19–34.

    Article  PubMed  Google Scholar 

Download references

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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Authors and Affiliations

  1. 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

    Xiangrong Zhou, Takeshi Hara, Ryujiro Yokoyama & Hiroshi Fujita

  2. Graduate School of Medicine, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi, 755-8611, Japan

    Rui Xu, Yasushi Hirano & Shoji Kido

  3. Department of Radiology, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan

    Masayuki Kanematsu

  4. Department of Radiology, Division of Tumor Control, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Hiroaki Hoshi

Authors
  1. Xiangrong Zhou
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  2. Rui Xu
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  3. Takeshi Hara
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  4. Yasushi Hirano
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  5. Ryujiro Yokoyama
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  6. Masayuki Kanematsu
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  7. Hiroaki Hoshi
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  8. Shoji Kido
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  9. Hiroshi Fujita
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Corresponding author

Correspondence to Xiangrong Zhou.

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Zhou, X., Xu, R., Hara, T. et al. Development and evaluation of statistical shape modeling for principal inner organs on torso CT images. Radiol Phys Technol 7, 277–283 (2014). https://doi.org/10.1007/s12194-014-0261-6

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  • DOI: https://doi.org/10.1007/s12194-014-0261-6

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