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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 4 — Apr. 1, 2014
  • pp: 1062–1074

Multiple-object geometric deformable model for segmentation of macular OCT

Aaron Carass, Andrew Lang, Matthew Hauser, Peter A. Calabresi, Howard S. Ying, and Jerry L. Prince  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 4, pp. 1062-1074 (2014)
http://dx.doi.org/10.1364/BOE.5.001062


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Abstract

Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed deformable model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(170.4470) Medical optics and biotechnology : Ophthalmology
(170.4500) Medical optics and biotechnology : Optical coherence tomography

ToC Category:
Image Processing

History
Original Manuscript: January 1, 2014
Revised Manuscript: February 9, 2014
Manuscript Accepted: February 21, 2014
Published: March 4, 2014

Citation
Aaron Carass, Andrew Lang, Matthew Hauser, Peter A. Calabresi, Howard S. Ying, and Jerry L. Prince, "Multiple-object geometric deformable model for segmentation of macular OCT," Biomed. Opt. Express 5, 1062-1074 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-4-1062


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References

  1. E. Gordon-Lipkin, B. Chodkowski, D. S. Reich, S. A. Smith, M. Pulicken, L. J. Balcer, E. M. Frohman, G. Cutter, and P. A. Calabresi, “Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis,” Neurology69, 1603–1609 (2007). [CrossRef] [PubMed]
  2. S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain134, 518–533 (2011). [CrossRef] [PubMed]
  3. S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” The Lancet Neurology11, 963–972 (2012). [CrossRef]
  4. S. Saidha, S. B. Syc, M. K. Durbin, C. Eckstein, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. Newsome, J. N. Ratchford, E. M. Frohman, and P. A. Calabresi, “Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness,” Mult. Scler.17, 1449–1463 (2011). [CrossRef] [PubMed]
  5. J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994). [CrossRef] [PubMed]
  6. A. J. Green, S. McQuaid, S. L. Hauser, I. V. Allen, and R. Lyness, “Ocular pathology in multiple sclerosis: retinal atrophy and inflammation irrespective of disease duration,” Brain133, 1591–1601 (2010). [CrossRef] [PubMed]
  7. J. S. Schuman, H. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, J. A. I. E. Hertzmark, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography,” Arch. Ophthalmol.113, 586–596 (1995). [CrossRef] [PubMed]
  8. H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology117, 1692–1699 (2010). [CrossRef] [PubMed]
  9. S. C. Park, C. G. V. De Moraes, C. C. Teng, C. Tello, J. M. Liebmann, and R. Ritch, “Enhanced depth imaging optical coherence tomography of deep optic nerve complex structures in glaucoma,” Ophthalmology119, 3–9 (2012). [CrossRef]
  10. A. Kanamori, M. Nakamura, M. F. T. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. of Ophthalmol.135, 513–520 (2003). [CrossRef]
  11. S. H. Kang, S. W. Hong, S. K. Im, S. H. Lee, and M. D. Ahn, “Effect of myopia on the thickness of the retinal nerve fiber layer measured by Cirrus HD optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.51, 4075–4083 (2010). [CrossRef] [PubMed]
  12. V. J. Srinivasan, S. Sakadžić, I. Gorczynska, S. Ruvinskaya, W. Wu, J. G. Fujimoto, and D. A. Boas, “Quantitative cerebral blood flow with optical coherence tomography,” Opt. Express18, 2477–2494 (2010). [CrossRef] [PubMed]
  13. D. Y. Kim, J. Fingler, J. S. Werner, D. M. Schwartz, S. E. Fraser, and R. J. Zawadzki, “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography,” Biomed. Opt. Express2, 1504–1513 (2011). [CrossRef] [PubMed]
  14. L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010). [CrossRef]
  15. Y. Lu, Z. Li, X. Zhang, B. Ming, J. Jia, R. Wang, and D. Ma, “Retinal nerve fiber layer structure abnormalities in early Alzheimer’s disease: Evidence in optical coherence tomography,” Neurosci. Lett.480, 69–72 (2010). [CrossRef] [PubMed]
  16. A. Kesler, V. Vakhapova, A. D. Korczyn, E. Naftaliev, and M. Neudorfer, “Retinal thickness in patients with mild cognitive impairment and Alzheimer’s disease,” Clin. Neurol. Neurosurgery113, 523–526 (2011). [CrossRef]
  17. T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology117, 2379–2386 (2010). [CrossRef] [PubMed]
  18. D. C. De Buc and G. M. Somfal, “Early detection of retinal thickness changes in diabetes using Optical Coherence Tomography,” Med. Sci. Monit.16, 15–21 (2010).
  19. I. Ghorbel, F. Rossant, I. Bloch, and M. Paques, “Modeling a parallelism constraint in active contours. Application to the segmentation of eye vessels and retinal layers,” in Image Processing (ICIP), 2011 18th IEEE International Conference on,” (2011), pp. 445–448.
  20. I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recognition44, 1590–1603 (2011). [CrossRef]
  21. V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and W. Drexler, “Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express18, 14730–14744 (2010). [CrossRef]
  22. D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imag.20, 900–916 (2001). [CrossRef]
  23. K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express2, 1743–1756 (2011). [CrossRef] [PubMed]
  24. A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express4, 1133–1152 (2013). [CrossRef] [PubMed]
  25. B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” Proc. SPIE8314, 83141G (2012). [CrossRef]
  26. P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Zanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imag.32, 531–543 (2013). [CrossRef]
  27. Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag.32, 376–386 (2013). [CrossRef]
  28. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18, 19413–19428 (2010). [CrossRef] [PubMed]
  29. M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imag.27, 1495–1505 (2008). [CrossRef]
  30. M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag.28, 1436–1447 (2009). [CrossRef]
  31. M. A. Mayer, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients,” Biomed. Opt. Express1, 1358–1383 (2010). [CrossRef]
  32. A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express17, 23719–23728 (2009). [CrossRef]
  33. Q. Yang, C. A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Express18, 21293–21307 (2010). [CrossRef] [PubMed]
  34. M. Chen, A. Lang, E. Sotirchos, H. S. Ying, P. A. Calabresi, J. L. Prince, and A. Carass, “Deformable registration of macular OCT using A-mode scan similarity,” in 10th International Symposium on Biomedical Imaging (ISBI 2013),” (2013), pp. 476–479.
  35. J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in 10th International Symposium on Biomedical Imaging (ISBI 2013),” (2013), pp. 998–1001.
  36. A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” Proc. SPIE8669, 86690R (2013). [CrossRef]
  37. A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” Proc. SPIE9034, 90340A (2014).
  38. J. W. Gibbs, “Fourier’s series,” Nature59, 200 (1898). [CrossRef]
  39. J. A. Bogovic, J. L. Prince, and P.-L. Bazin, “A multiple object geometric deformable model for image segmentation,” Comput. Vis. Image Und.117, 145–157 (2013).
  40. L. Breiman, “Random forests,” Machine Learning45, 5–32 (2001). [CrossRef]
  41. V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993). [CrossRef]
  42. C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Imag. Proc.7, 359–369 (1998). [CrossRef]
  43. X. Han, C. Xu, and J. L. Prince, “A topology preserving level set method for geometric deformable models,” IEEE Trans. Pattern Anal. Mach. Intell.25, 755–768 (2003). [CrossRef]
  44. P.-L. Bazin, L. Ellingsen, and D. Pham, “Digital homeomorphisms in deformable registration,” in 20th Inf. Proc. in Med. Imaging (IPMI 2007),” (2007), pp. 211–222.
  45. B. C. Lucas, M. Kazhdan, and R. H. Taylor, “Multi-object geodesic active contours (MOGAC),” in 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2012),” (2012), pp. 404–412.
  46. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945). [CrossRef]

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