Advertisement

Globally Optimal Label Fusion with Shape Priors

  • Ipek OguzEmail author
  • Satyananda Kashyap
  • Hongzhi Wang
  • Paul Yushkevich
  • Milan Sonka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach. We demonstrate GOLF for the joint segmentation of the left and right pairs of caudate, putamen, globus pallidus and nucleus accumbens. Compared to the FreeSurfer and FIRST approaches, GOLF is significantly more accurate on all reported indices for all 8 structures. We also present comparisons to a multi-atlas approach, which reveals further insights on the contributions of the different components of the proposed framework.

Notes

Acknowledgments

This work was funded by NIH grants EB004640 and EB017255.

References

  1. 1.
    Awate, S.P., Whitaker, R.T.: Multiatlas segmentation as nonparametric regression. IEEE TMI 33(9), 1803–1817 (2014)Google Scholar
  2. 2.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: ICCV, pp. 377–384 (1999)Google Scholar
  4. 4.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  5. 5.
    Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: A survey. Med. Image Anal. 24(1), 205–219 (2015)CrossRefGoogle Scholar
  6. 6.
    Koch, L.M., Rajchl, M., Tong, T., Passerat-Palmbach, J., Aljabar, P., Rueckert, D.: Multi-atlas segmentation as a graph labelling problem: application to partially annotated atlas data. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 221–232. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19992-4_17 CrossRefGoogle Scholar
  7. 7.
    Patenaude, B., Smith, S., Kennedy, D., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56(3), 907–922 (2011)CrossRefGoogle Scholar
  8. 8.
    Paulsen, J.S., Long, J.D., Johnson, H.J.: Clinical and biomarker changes in premanifest HD show trial feasibility: a decade of the PREDICT-HD study. Front. Aging 6, 78 (2014)Google Scholar
  9. 9.
    Wachinger, C., Sharp, G.C., Golland, P.: Contour-driven regression for label inference in atlas-based segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 211–218. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40760-4_27 CrossRefGoogle Scholar
  10. 10.
    Wang, H., Suh, J.W., Das, S.R., Pluta, J., Craige, C., Yushkevich, P.A.: Multi-Atlas segmentation with joint label fusion. IEEE PAMI 35(3), 611–623 (2012)CrossRefGoogle Scholar
  11. 11.
    Yin, Y., Zhang, X., Williams, R., Wu, X., Anderson, D.D., Sonka, M.: LOGISMOS. IEEE Trans. Med. Imaging 29(12), 2023–2037 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ipek Oguz
    • 1
    • 2
    Email author
  • Satyananda Kashyap
    • 2
  • Hongzhi Wang
    • 3
  • Paul Yushkevich
    • 1
  • Milan Sonka
    • 2
  1. 1.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Iowa Institute for Biomedical ImagingUniversity of IowaIowa CityUSA
  3. 3.IBM ResearchAlmadenUSA

Personalised recommendations