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Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics

  • Prasanna Muralidharan
  • James Fishbaugh
  • Hans J. Johnson
  • Stanley Durrleman
  • Jane S. Paulsen
  • Guido Gerig
  • P. Thomas Fletcher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Longitudinal imaging studies involve tracking changes in individuals by repeated image acquisition over time. The goal of these studies is to quantify biological shape variability within and across individuals, and also to distinguish between normal and disease populations. However, data variability is influenced by outside sources such as image acquisition, image calibration, human expert judgment, and limited robustness of segmentation and registration algorithms. In this paper, we propose a two-stage method for the statistical analysis of longitudinal shape. In the first stage, we estimate diffeomorphic shape trajectories for each individual that minimize inconsistencies in segmented shapes across time. This is followed by a longitudinal mixed-effects statistical model in the second stage for testing differences in shape trajectories between groups. We apply our method to a longitudinal database from PREDICT-HD and demonstrate our approach reduces unwanted variability for both shape and derived measures, such as volume. This leads to greater statistical power to distinguish differences in shape trajectory between healthy subjects and subjects with a genetic biomarker for Huntington’s disease (HD).

Keywords

Huntington Disease Spatiotemporal Model Anatomical Shape Shape Trajectory Sasaki Metrics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Davis, B., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. In: Proceedings of IEEE International Conference on Computer Vision (2007)Google Scholar
  2. 2.
    Niethammer, M., Huang, Y., Vialard, F.X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Fletcher, P.T.: Geodesic regression and the theory of least squares on riemannian manifolds. Int. J. Comput. Vision 105(2), 171–185 (2013)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Hinkle, J., Muralidharan, P., Fletcher, P.T., Joshi, S.: Polynomial regression on Riemannian manifolds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 1–14. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Qiu, A., Albert, M., Younes, L., Miller, M.: Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes. NeuroImage 45, S51–S60 (2009)Google Scholar
  6. 6.
    Muralidharan, P., Fletcher, P.T.: Sasaki metrics for the analysis of longitudinal data on manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  7. 7.
    Datar, M., Muralidharan, P., Kumar, A., Gouttard, S., Piven, J., Gerig, G., Whitaker, R., Fletcher, P.T.: Mixed-effects shape models for estimating longitudinal changes in anatomy. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds.) STIA 2012. LNCS, vol. 7570, pp. 76–87. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    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. Vision 103(1), 22–59 (2013)CrossRefzbMATHGoogle Scholar
  9. 9.
    Fishbaugh, J., Prastawa, M., Gerig, G., Durrleman, S.: Geodesic Shape Regression in the Framework of Currents. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 718–729. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape modeling and analysis with entropy-based particle systems. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 333–345. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Aylward, E., Mills, J., Liu, D., Nopoulos, P., Ross, C.A., Pierson, R., Paulsen, J.S.: Association between Age and Striatal Volume Stratified by CAG Repeat Length in Prodromal Huntington Disease. PLoS Curr. 3, RRN1235 (2011)Google Scholar
  12. 12.
    Kim, E.Y., Johnson, H.J.: Robust multi-site mr data processing: Iterative optimization of bias correction, tissue classification, and registration. Frontiers in Neuroinformatics 7(29) (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Prasanna Muralidharan
    • 1
  • James Fishbaugh
    • 1
  • Hans J. Johnson
    • 2
  • Stanley Durrleman
    • 3
  • Jane S. Paulsen
    • 2
  • Guido Gerig
    • 1
  • P. Thomas Fletcher
    • 1
  1. 1.School of Computing & SCI InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.Department of Psychiatry, Carver College of MedicineUniversity of IowaIowa CityUSA
  3. 3.Inria Paris-Rocquencourt, Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, ICMParisFrance

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