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

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  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]

2014 (1)

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).

2013 (5)

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).

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]

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]

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]

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]

2012 (3)

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]

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]

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]

2011 (6)

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]

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]

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]

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]

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]

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]

2010 (12)

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]

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]

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]

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]

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]

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]

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).

L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010).
[CrossRef]

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]

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]

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]

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]

2009 (2)

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]

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]

2008 (1)

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]

2007 (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]

2003 (2)

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]

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]

2001 (2)

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]

L. Breiman, “Random forests,” Machine Learning45, 5–32 (2001).
[CrossRef]

1998 (1)

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Imag. Proc.7, 359–369 (1998).
[CrossRef]

1995 (1)

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]

1994 (1)

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
[CrossRef] [PubMed]

1993 (1)

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945).
[CrossRef]

1898 (1)

J. W. Gibbs, “Fourier’s series,” Nature59, 200 (1898).
[CrossRef]

Abdillahi, H.

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]

Abràmoff, M. D.

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]

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]

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]

Ahn, M. D.

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]

Alasil, T.

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]

Alencar, L. M.

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]

Allen, I. V.

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]

Antony, B. J.

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]

Araie, M.

Bai, J.

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]

Balcer, L. J.

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]

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]

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]

Bazin, P.-L.

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).

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.

Bizheva, K.

Bloch, I.

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]

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.

Boas, D. A.

Bogovic, J. A.

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).

Boyer, K.

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]

Breiman, L.

L. Breiman, “Random forests,” Machine Learning45, 5–32 (2001).
[CrossRef]

Buatti, J. M.

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]

Burns, T. L.

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]

Calabresi, P.

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]

Calabresi, P. A.

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).

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]

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]

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]

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]

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]

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.

Carass, A.

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).

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]

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]

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.

Caselles, V.

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

Catté, F.

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

Ceklic, L.

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]

Chan, K.

Chen, M.

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.

Chiu, S. J.

Chodkowski, B.

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]

Clausi, D. A.

Coll, T.

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

Conger, A.

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]

Cordeiro, M. F.

L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010).
[CrossRef]

Crainiceanu, C. M.

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]

Cutter, G.

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]

de Boer, J. F.

De Buc, D. C.

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).

De Moraes, C. G. V.

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]

Dibos, F.

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

Dice, L. R.

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945).
[CrossRef]

Drexler, W.

Dufour, P. A.

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]

Duggan, J.

L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010).
[CrossRef]

Durbin, M. K.

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]

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]

Dustin, L.

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]

Eckstein, C.

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]

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]

Ellingsen, L.

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.

Escano, M. F. T.

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]

Farrell, S. K.

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]

Farsiu, S.

Fingler, J.

Flynn, T.

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
[CrossRef] [PubMed]

Fraser, S. E.

Frohman, E. M.

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]

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]

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]

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]

Frohman, T. C.

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]

Fujimoto, J. G.

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]

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]

Fukuma, Y.

Garvin, M. K.

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]

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]

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]

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]

Gelfand, J. M.

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]

Ghorbel, I.

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]

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.

Gibbs, J. W.

J. W. Gibbs, “Fourier’s series,” Nature59, 200 (1898).
[CrossRef]

Gorczynska, I.

Gordon-Lipkin, E.

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]

Green, A. J.

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]

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]

Green, W. R.

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
[CrossRef] [PubMed]

Guo, L.

L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010).
[CrossRef]

Han, X.

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]

Hangai, M.

Hauser, M.

Hauser, S. L.

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]

Hee, H. R.

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]

Hermann, B.

Hertzmark, J. A. I. E.

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]

Hofer, B.

Hong, S. W.

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]

Hood, D. C.

Hornegger, J.

Ibrahim, M. A.

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]

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]

Im, S. K.

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]

Izatt, J. A.

Jia, J.

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]

Kajic, V.

Kanamori, A.

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]

Kang, S. H.

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]

Kardon, R.

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]

Kazhdan, M.

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.

Keane, P. A.

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]

Kerrison, J. B.

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
[CrossRef] [PubMed]

Kesler, A.

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]

Kim, D. Y.

Koozekanani, D.

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]

Korczyn, A. D.

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]

Kowal, J.

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]

Kwon, Y. H.

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]

Lang, A.

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).

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]

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]

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.

Lee, S. H.

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]

Lemij, H. G.

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]

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.

Li, X. T.

Li, Z.

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]

Liebmann, J. M.

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]

Lin, C. P.

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]

Lu, Y.

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]

Lucas, B. C.

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.

Lyness, R.

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]

Ma, D.

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]

Maeda, H.

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]

Mardin, C. Y.

Marshall, D.

Mayer, M. A.

McQuaid, S.

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]

Medeiros, F. A.

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]

Meyer, S. A.

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]

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]

Ming, B.

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]

Mishra, A.

Naftaliev, E.

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]

Nakamura, M.

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]

Negi, A.

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]

Neudorfer, M.

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]

Newsome, S.

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]

Newsome, S. D.

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]

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]

Nguyen, Q. D.

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]

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]

Nicholas, P.

Novosel, J.

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.

Oakley, J. D.

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]

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]

Oh, J.

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]

Ouyang, Y.

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]

Paques, M.

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]

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.

Park, S. C.

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]

Pedut-Kloizman, T.

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]

Pham, D.

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.

Považay, B.

Prince, J. L.

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).

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]

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).

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]

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]

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Imag. Proc.7, 359–369 (1998).
[CrossRef]

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.

Puliafito, C. A.

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]

Pulicken, M.

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]

Rao, H. L.

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]

Ratchford, J. N.

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]

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]

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]

Raza, A. S.

Reich, D. S.

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]

Reisman, C. A.

Ritch, R.

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]

Roberts, C.

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]

Rosenzweig, J. M.

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]

Rosin, P. L.

Rossant, F.

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]

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.

Russell, S. R.

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]

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]

Ruvinskaya, S.

Sadda, S. R.

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]

Saidha, S.

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]

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]

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]

Sakadžic, S.

Sample, P. A.

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]

Schroder, S.

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]

Schuman, J. S.

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]

Schwartz, D. M.

Seigo, M. A.

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]

Sepah, Y. J.

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]

Seya, R.

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]

Smith, S. A.

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]

Somfal, G. M.

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).

Song, Q.

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]

Sonka, M.

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]

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]

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]

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]

Sotirchos, E.

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]

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.

Sotirchos, E. S.

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]

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]

Srinivasan, V. J.

Swanson, E. A.

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]

Syc, S. B.

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]

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]

Taylor, R. H.

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.

Tello, C.

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]

Teng, C. C.

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]

Thepass, G.

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.

Tick, S.

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]

Tomidokoro, A.

Tornow, R. P.

Toth, C. A.

Updike, J. F.

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]

Vakhapova, V.

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]

van der Schoot, J.

van Vliet, L. J.

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.

Vermeer, K. A.

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]

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.

Walsh, A. C.

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]

Wang, R.

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]

Wang, Z.

Warner, C. V.

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]

Weinreb, R. N.

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]

Werner, J. S.

Wolf-Schnurrbusch, U.

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]

Wong, A.

Wong, C.

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]

Wu, W.

Wu, X.

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]

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]

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]

Xu, C.

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]

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Imag. Proc.7, 359–369 (1998).
[CrossRef]

Yang, Q.

Ying, H. S.

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).

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]

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.

Yoshimura, N.

Zanet, S. D.

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]

Zangwill, L. M.

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]

Zawadzki, R. J.

Zhang, X.

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]

Am. J. of Ophthalmol. (1)

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]

Arch. Ophthalmol. (1)

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]

Biomed. Opt. Express (4)

Brain (2)

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]

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]

Clin. Neurol. Neurosurgery (1)

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]

Comput. Vis. Image Und. (1)

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).

Current Alzheimer Research (1)

L. Guo, J. Duggan, and M. F. Cordeiro, “Alzheimer’s disease and retinal neurodegeneration,” Current Alzheimer Research7, 3–14 (2010).
[CrossRef]

Ecology (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945).
[CrossRef]

IEEE Trans. Imag. Proc. (1)

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Imag. Proc.7, 359–369 (1998).
[CrossRef]

IEEE Trans. Med. Imag. (5)

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]

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]

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]

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]

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]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

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]

Invest. Ophthalmol. Vis. Sci. (1)

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]

Machine Learning (1)

L. Breiman, “Random forests,” Machine Learning45, 5–32 (2001).
[CrossRef]

Med. Sci. Monit. (1)

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).

Mult. Scler. (1)

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]

Nature (1)

J. W. Gibbs, “Fourier’s series,” Nature59, 200 (1898).
[CrossRef]

Neurology (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]

Neurosci. Lett. (1)

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]

Numerische Mathematik (1)

V. Caselles, F. Catté, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathematik66, 1–31 (1993).
[CrossRef]

Ophthalmology (3)

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]

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]

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]

Opt. Express (5)

Pattern Recognition (1)

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]

Proc. SPIE (3)

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]

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]

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).

Retina (1)

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
[CrossRef] [PubMed]

The Lancet Neurology (1)

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]

Other (5)

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.

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.

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.

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.

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.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (4)

Fig. 1
Fig. 1

(a) A B-scan from a subject in our cohort with annotations indicating the locations of the vitreous, choroid, clivus, and fovea. (The image has been rescaled by a factor of three along each A-scan for display purposes.) The red boxed region is shown magnified (×3) in (b) with markings to denote various layers and boundaries. The layers are: RNFL; ganglion cell (GCL); inner plexiform (IPL); inner nuclear (INL); outer plexiform (OPL); outer nuclear (ONL), inner segment (IS); outer segment (OS); retinal pigment epithelium (RPE). The named boundaries are: inner limiting membrane (ILM); external limiting membrane (ELM); Bruch’s Membrane (BrM). The OS and RPE are collectively referred to as the hyper-reflectivity complex (HRC), and the ganglion cell complex (GCC) comprises the RNFL, GCL, and IPL.

Fig. 2
Fig. 2

Shown is (a) the original image in native space. In flat space are (b) the original image, (c) a heat map of the probabilities for one of the boundaries (ILM), and (d) the y-component of the GVF field for that same boundary. The color scale in (c) represents zero as blue and one as red.

Fig. 3
Fig. 3

Shown in flat space are (a) the MGDM initialization, and (b) the MGDM result. The MGDM result mapped back to the native space of the subject is shown in (d) and for comparison the manual segmentation of the same subject is shown in (c). The same color map is used in this figure and in Fig. 4.

Fig. 4
Fig. 4

Shown is a magnified (×18) region around the fovea for each of (a) the original image, (b) the manual delineation, and automated segmentations generated by (c) RF+Graph [24] and (d) our method. The result in (d) is generated from the continuous representation of the level sets in the subjects native space, shown in (e) is the voxelated equivalent for our method. The RF+Graph method has to keep each layer at least one voxel thick (the GCIP and INL in this case). We also observe the voxelated nature of the the RF+Graph result, whereas our approach has a continuous representation due to its use of levelsets shown in (d) but can also be converted a voxelated format (e). The same color map is used in this figure and in Fig. 3.

Tables (2)

Tables Icon

Table 1 Mean (and standard deviations) of the Dice Coefficient across the eight retinal layers. A paired Wilcoxon rank sum test was used to test the significance of any improvement between RF+Graph [24] and our method, with strong significance (an α level of 0.001) in six of the eight layers.

Tables Icon

Table 2 Mean absolute errors (and standard deviation) in microns for our method (MGDM) in comparison to RF+Graph [24] on the nine estimated boundaries. A paired Wilcoxon rank sum test was used to compute p-values between the two methods with strong significance (an α level of 0.001) in six of the nine boundaries.

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

ϕ i ( x ) = { d x ( O i ) , x O i , d x ( O i ) , x O i ,
d x ( O i ) = min y O i x y
ϕ i ( x ) t = f i ( x ) | ϕ i ( x ) | .
L 0 ( x ) = i x O i L 1 ( x ) = argmin j L 0 ( x ) d x ( O j ) L 2 ( x ) = argmin j { L 0 ( x ) , L 1 ( x ) } d x ( O j ) L N 1 ( x ) = argmin j { L k ( x ) } k = 0 , , N 2 d x ( O j ) .
φ 0 ( x ) = d x ( L 1 ( x ) ) φ 1 ( x ) = d x ( L 2 ( x ) ) d x ( L 1 ( x ) ) φ 2 ( x ) = d x ( L 3 ( x ) ) d x ( L 2 ( x ) ) φ N 2 ( x ) = d x ( L N 1 ( x ) ) d x ( L N 2 ( x ) )
ϕ i ^ ( x ) = { φ 0 ( x ) , i = L 0 ( x ) φ 0 ( x ) , i = L 1 ( x ) φ 0 ( x ) + φ 1 ( x ) , i = L 2 ( x ) φ 0 ( x ) + φ 1 ( x ) + φ 2 ( x ) , i L 0 , 1 , 2 ( x ) ,
ψ k ( x ) t = 1 2 ( f L k ( x ) f L k + 1 ( x ) ) , k = 2 , 1 , 0 ,

Metrics