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Automated 3D Segmentation of Multiple Surfaces with a Shared Hole: Segmentation of the Neural Canal Opening in SD-OCT Volumes

  • Bhavna J. Antony
  • Mohammed S. Miri
  • Michael D. Abràmoff
  • Young H. Kwon
  • Mona K. Garvin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

The need to segment multiple interacting surfaces is a common problem in medical imaging and it is often assumed that such surfaces are continuous within the confines of the region of interest. However, in some application areas, the surfaces of interest may contain a shared hole in which the surfaces no longer exist and the exact location of the hole boundary is not known a priori. The boundary of the neural canal opening seen in spectral-domain optical coherence tomography volumes is an example of a “hole” embedded with multiple surrounding surfaces. Segmentation approaches that rely on finding the surfaces alone are prone to failures as deeper structures within the hole can “attract” the surfaces and pull them away from their correct location at the hole boundary. With this application area in mind, we present a graph-theoretic approach for segmenting multiple surfaces with a shared hole. The overall cost function that is optimized consists of both the costs of the surfaces outside the hole and the cost of boundary of the hole itself. The constraints utilized were appropriately adapted in order to ensure the smoothness of the hole boundary in addition to ensuring the smoothness of the non-overlapping surfaces. By using this approach, a significant improvement was observed over a more traditional two-pass approach in which the surfaces are segmented first (assuming the presence of no hole) followed by segmenting the neural canal opening.

Keywords

Optical Coherence Tomography Optic Nerve Head Projection Image Internal Limit Membrane Hole Boundary 
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.
    Garvin, M.K., Abràmoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009)CrossRefGoogle Scholar
  2. 2.
    Delong, A., Boykov, Y.: Globally optimal Segmentation of multi-region objects. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Number Iccv, pp. 285–92 (2009)Google Scholar
  3. 3.
    Strouthidis, N.G., Yang, H., Downs, J.C., Burgoyne, C.F.: Comparison of clinical and three-dimensional histomorphometric optic disc margin anatomy.. Invest. Ophthalmol. Vis. Sci. 50(5), 2165–2174 (2009)CrossRefGoogle Scholar
  4. 4.
    Hu, Z., Abràmoff, M.D., Kwon, Y.H., Lee, K., Garvin, M.K.: Automated Segmentation of Neural Canal Opening and Optic Cup in 3D Spectral Optical Coherence Tomography Volumes of the Optic Nerve Head. Invest. Ophthalmol. Vis. Sci. 51(11), 5708–5717 (2010)CrossRefGoogle Scholar
  5. 5.
    Boyer, K.L., Herzog, A., Roberts, C.: Automatic recovery of the optic nervehead geometry in optical coherence tomography. IEEE Trans. Med. Imag. 25(5), 553–570 (2006)CrossRefGoogle Scholar
  6. 6.
    Lee, K., Niemeijer, M., Garvin, M.K., Kwon, Y.H., Sonka, M., Abràmoff, M.D.: Segmentation of the optic disc in 3D-OCT scans of the optic nerve head. IEEE Trans. Image Process. 29(1), 159–168 (2009)Google Scholar
  7. 7.
    Song, Q., Bai, J., Garvin, M.K., Sonka, M., Buatti, J.M., Wu, X.: Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imag. 32(2), 376–386 (2013)CrossRefGoogle Scholar
  8. 8.
    Smith, S.M., Brady, J.M.: SUSAN - A New Approach to Low Level Image Processing. International Journal of Computer Vision 23, 45–78 (1995)CrossRefGoogle Scholar
  9. 9.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Reis, A.S.C., Sharpe, G.P., Yang, H., Nicolela, M.T., Burgoyne, C.F., Chauhan, B.C.: Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography. Ophthalmology 119(4), 738–747 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bhavna J. Antony
    • 1
  • Mohammed S. Miri
    • 1
  • Michael D. Abràmoff
    • 2
    • 3
    • 1
  • Young H. Kwon
    • 2
  • Mona K. Garvin
    • 3
    • 1
  1. 1.Electrical & Computer EngineeringThe University of IowaIowa CityUSA
  2. 2.Ophthalmology & Visual SciencesThe University of IowaIowa CityUSA
  3. 3.Iowa City VA Healthcare SystemIowa CityUSA

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