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Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data

  • Atsushi SaitoEmail author
  • Koyo Nakayama
  • Antonio R. Porras
  • Awais Mansoor
  • Elijah Biggs
  • Marius George Linguraru
  • Akinobu Shimizu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

This paper proposes a spatiotemporal statistical shape model of a pediatric liver, which has potential applications in computer-aided diagnosis of the abdomen. Shapes are analyzed in the space of a level set function, which has computational advantages over the diffeomorphic framework commonly employed in conventional studies. We first calculate the time-varying average of the mean shape development using a kernel regression technique with adaptive bandwidth. Then, eigenshape modes for every timepoint are calculated using principal component analysis with an additional regularization term that ensures the smoothness of the temporal change of the eigenshape modes. To further improve the performance, we applied data augmentation using a level set-based nonlinear morphing technique. The proposed algorithm was evaluated in the context of a spatiotemporal statistical shape modeling of a liver using 42 manually segmented livers from children whose age ranged from approximately 2 weeks to 95 months. Our method achieved a higher generalization and specificity ability compared with conventional methods.

Keywords

Spatiotemporal analysis Statistical shape model Pediatric Liver 

Notes

Acknowledgments

This work is partly supported by KAKENHI (No. 26108002, 16H06785 and 18H03255) and the Sheikh Zayed Institute at Children’s National Health System.

References

  1. 1.
    Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)CrossRefGoogle Scholar
  2. 2.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. Int. J. Comput. Vis. 90(2), 255–266 (2010)CrossRefGoogle Scholar
  3. 3.
    Serag, A., et al.: Construction of a consistent highdefinition spatio-temporal atlas of the developing brain using adaptive kernel regression. Neuroimage 59(3), 2255–2265 (2012)CrossRefGoogle Scholar
  4. 4.
    Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. Int. J. Comput. Vis. 103(1), 22–59 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mansi, T., et al.: A statistical model of right ventricle in tetralogy of fallot for prediction of remodelling and therapy planning. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 214–221. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04268-3_27CrossRefGoogle Scholar
  6. 6.
    Qiu, A., Albert, M., Younes, L., Miller, M.I.: Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes. Neuroimage 45(1), S51–S60 (2009)CrossRefGoogle Scholar
  7. 7.
    Kishimoto, M., et al.: A spatiotemporal statistical model for eyeballs of human embryos. IEICE Trans. Inf. Syst. 100(7), 1505–1515 (2017)CrossRefGoogle Scholar
  8. 8.
    Alam, S., Kobashi, S., Nakano, R., Morimoto, M., Aikawa1, S., Shimizu, A.: Spatiotemporal statistical shape model construction for longitudinal brain deformation analysis using weighted PCA. In: Computer Assisted Radiology and Surgery (CARS) 2016, vol. 11, p. S204 (2016)Google Scholar
  9. 9.
    Younes, L.: Jacobi fields in groups of diffeomorphisms and applications. Q. Appl. Math. 65(1), 113–134 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Breen, D.E., Whitaker, R.T.: A level-set approach for the metamorphosis of solid models. IEEE Trans. Vis. Comput. Graph. 7(2), 173–192 (2001)CrossRefGoogle Scholar
  11. 11.
    Saito, A., Nakada, M., Oost, E., Shimizu, A., Watanabe, H., Nawano, S.: A statistical shape model for multiple organs based on synthesized-based learning. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds.) ABD-MICCAI 2013. LNCS, vol. 8198, pp. 280–289. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41083-3_31CrossRefGoogle Scholar
  12. 12.
    Styner, M.A.: Evaluation of 3D correspondence methods for model building. In: Taylor, C., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 63–75. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-45087-0_6CrossRefGoogle Scholar
  13. 13.
    Uchida, Y., Shimizu, A., Kobatake, H., Nawano, S., Shinozaki, K.: A comparative study of statistical shape models of the pancreas. In: Computer Assisted Radiology and Surgery (CARS) 2010, vol. 5, pp. S385–S387 (2010)Google Scholar
  14. 14.
    Nadaraya, E.A.: On estimating regression. Theory Probab. Appl. 9(1), 141–142 (1964)CrossRefGoogle Scholar
  15. 15.
    Demir, S., Toktamiş, Ö.: On the adaptive Nadaraya-Watson kernel regression estimators. Hacet. J. Math. Stat. 39(3), 429–437 (2010)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89(1), 149–185 (2000)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Health SystemWashington, D.C.USA
  3. 3.School of Medicine and Health SciencesGeorge Washington UniversityWashington, D.C.USA

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