Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters

  • Adrian Dalca
  • Giovanna Danagoulian
  • Ron Kikinis
  • Ehud Schmidt
  • Polina Golland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

Keywords

nerve bundles tracking segmentation particle filter 

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References

  1. 1.
    Aylward, S.R., Bullitt, E.: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans. Med. Imag. 21(2), 61–75 (2002)CrossRefGoogle Scholar
  2. 2.
    Balbi, V., Budzik, J.F., Duhamel, A., Bera-Louville, A., Le Thuc, V., Cotten, A.: Tractography of lumbar nerve roots: initial results. Eur. Radiol 21, 1153–1159 (2010)CrossRefGoogle Scholar
  3. 3.
    Behrens, T., Rohr, K., Stiehl, H.S.: Segmentation of tubular structures in 3d images using a combination of the hough transform and a kalman filter. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 406–413. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Benmansour, F., Cohen, L.D.: A new interactive method for coronary arteries segmentation based on tubular anisotropy. In: ISBI 2009, pp. 41–44 (2009)Google Scholar
  5. 5.
    Boskamp, T., Rinck, D., Link, F., Kummerlen, B., Stamm, G., Mildenberger, P.: New Vessel Analysis Tool for Morphometric Quantification and Visualization of Vessels in CT and MR Imaging Data Sets. Radiographics 24(1), 287–297 (2004)CrossRefGoogle Scholar
  6. 6.
    Bruijns, J.: Fully-automatic branch labelling of voxel vessel structures. In: Vision Modeling and Vis. 2001, pp. 341–350 (2001)Google Scholar
  7. 7.
    Delingette, H., Montagnat, J.: Shape and topology constraints on parametric active contours. Computer Vision and Image Und. 83, 140–171 (2000)CrossRefMATHGoogle Scholar
  8. 8.
    Florin, C., Paragios, N., Williams, J.: Particle filters, a quasi-monte carlo solution for segmentation of coronaries. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 246–253. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Fridman, Y., Pizer, S.M., Aylward, S., Bullitt, E.: Segmenting 3d branching tubular structures using cores. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 570–577. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Gulsun, M., Tek, H.: Robust vessel tree modeling. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 602–611. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Lesagea, D., Angelini, E.D., Bloch, I., Funka-Leaa, G.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  12. 12.
    Li, H., Yezzi, A.: Vessels as 4-d curves: Global minimal 4-d paths to extract 3-d tubular surfaces and centerlines. IEEE Trans. Med. Imag. 26(9), 1213–1223 (2007)CrossRefGoogle Scholar
  13. 13.
    Lorigo, L.M., Faugeras, O.D., Grimson, E.L., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.-F.: Curves: Curve evolution for vessel segmentation. Med. Image Anal. 5(3), 195–206 (2001)CrossRefGoogle Scholar
  14. 14.
    Mille, J., Boné, R., Cohen, L.D.: Region-based 2d deformable generalized cylinder for narrow structures segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 392–404. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Schaap, M., Manniesing, R., Smal, I., van Walsum, T., van der Lugt, A., Niessen, W.: Bayesian tracking of tubular structures and its application to carotid arteries in CTA. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 562–570. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Schmidt, E.J., Shankaranarayanan, A., Jaume, S., Danagoulian, G., Mukundan, S.J., Nayak, K.S.: Wide-band steady state free precession with small diffusion gradients for spine imaging: Application to superior nerve visualization. In: 18th ISMRM, p. 448 (2010)Google Scholar
  17. 17.
    Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear / non-Gaussian Bayesian state estimation. IEE Proc-F 140(2), 107–113 (1993)Google Scholar
  18. 18.
    Bartels, R.H., Beatty, J.C., Barsky, B.A.: Bézier Curves (Ch. 10): An Introduction to Splines for Use in Computer Graphics and Geometric Modelling, pp. 211–245. Morgan Kauf., SF (1998)Google Scholar
  19. 19.
    Tyrrell, J., di Tomaso, E., Fuja, D., Tong, R., Kozak, K., Jain, R., Roysam, B.: Robust 3-d modeling of vasculature imagery using superellipsoids. IEEE Trans. Med. Imag. 26(2), 223–237 (2007)CrossRefGoogle Scholar
  20. 20.
    Yi, J., Ra, J.B.: A locally adaptive region growing algorithm for vascular segmentation. Int. J. Imag. Syst. Tech. 13(4), 208–214 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adrian Dalca
    • 1
  • Giovanna Danagoulian
    • 2
  • Ron Kikinis
    • 2
    • 3
  • Ehud Schmidt
    • 2
  • Polina Golland
    • 1
  1. 1.MIT Computer Science and Artificial InteligenceCambridgeUSA
  2. 2.Department of RadiologyBrigham and Women’s HospitalBostonUSA
  3. 3.Surgical Planning LaboratoryBrigham and Women’s HospitalBostonUSA

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