Abstract

Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2017 (3)

2016 (5)

I. Kopriva, F. Shi, and X. Chen, “Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography,” J. Biomed. Opt. 21(7), 076008 (2016).
[Crossref] [PubMed]

F. Shi, B. Tian, W. Zhu, D. Xiang, L. Zhou, H. Xu, and X. Chen, “Automated choroid segmentation in three-dimensional 1-μm wide-view OCT images with gradient and regional costs,” J. Biomed. Opt. 21(12), 126017 (2016).
[Crossref] [PubMed]

Z. Hu, C. A. Girkin, A. Hariri, and S. R. Sadda, “Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information,” Proc. SPIE 9697, 96971S (2016).
[Crossref]

P. S. Mittapalli and G. B. Kande, “Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma,” Biomed. Signal Process. Control 24, 34–46 (2016).
[Crossref]

L. Chakrabarty, G. D. Joshi, A. Chakravarty, G. V. Raman, S. R. Krishnadas, and J. Sivaswamy, “Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs,” J. Glaucoma 25(7), 590–597 (2016).
[Crossref] [PubMed]

2015 (2)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

2014 (3)

G. Staurenghi, S. Sadda, U. Chakravarthy, R. F. Spaide, and International Nomenclature for Optical Coherence Tomography (IN•OCT) Panel, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[Crossref] [PubMed]

Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The pathophysiology and treatment of glaucoma: a review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

2013 (5)

B. C. Chauhan and C. F. Burgoyne, “From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change,” Am. J. Ophthalmol. 156(2), 218–227 (2013).
[Crossref] [PubMed]

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. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

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. Imaging 32(2), 376–386 (2013).
[Crossref] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[Crossref] [PubMed]

2012 (3)

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-D optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31(7), 1337–1345 (2012).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

A. S. Reis, N. O’Leary, H. Yang, G. P. Sharpe, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation Clinically Invisible Optic Disc Margin Anatomy,” Invest. Ophthalmol. Vis. Sci. 53(4), 1852–1860 (2012).
[Crossref] [PubMed]

2011 (1)

J. C. Mwanza, J. D. Oakley, D. L. Budenz, D. R. Anderson, and Cirrus Optical Coherence Tomography Normative Database Study Group, “Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes,” Ophthalmology 118(2), 241–248 (2011).
[Crossref] [PubMed]

2010 (5)

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. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “Brief: Binary robust independent elementary features,” Comput. Vis. ECCV. 2010, 778–792 (2010).

2009 (4)

Z. Hu, M. Niemeijer, K. Lee, M. D. Abramoff, M. Sonka, and M. K. Garvin, “Automated segmentation of the optic disc margin in 3-D optical coherence tomography images using a graph-theoretic approach,” Proc. SPIE 7262, 72620U (2009).
[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. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

J. Nayak, R. Acharya U, P. S. Bhat, N. Shetty, and T. C. Lim, “Automated diagnosis of glaucoma using digital fundus images,” J. Med. Syst. 33(5), 337–346 (2009).
[Crossref] [PubMed]

N. G. Strouthidis, H. Yang, J. C. Downs, and C. F. Burgoyne, “Comparison of clinical and three-dimensional histomorphometric optic disc margin anatomy,” Invest. Ophthalmol. Vis. Sci. 50(5), 2165–2174 (2009).
[Crossref] [PubMed]

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. Imaging 27(10), 1495–1505 (2008).
[Crossref] [PubMed]

2007 (2)

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Vis. Sci. 48(7), 3195–3208 (2007).
[Crossref] [PubMed]

C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: illustrations, sources and a solution,” BMC Bioinformatics 8(1), 25 (2007).
[Crossref] [PubMed]

2006 (2)

H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol. 90(3), 262–267 (2006).
[Crossref] [PubMed]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

2004 (3)

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004).
[Crossref] [PubMed]

G. J. Jaffe and J. Caprioli, “Optical coherence tomography to detect and manage retinal disease and glaucoma,” Am. J. Ophthalmol. 137(1), 156–169 (2004).
[Crossref] [PubMed]

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[Crossref]

2002 (1)

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref] [PubMed]

2001 (1)

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

1996 (1)

H. A. Quigley, “Number of people with glaucoma worldwide,” Br. J. Ophthalmol. 80(5), 389–393 (1996).
[Crossref] [PubMed]

1995 (1)

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113(3), 325–332 (1995).
[Crossref] [PubMed]

1975 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. Syst. 11(285–296), 23– 27 (1975).

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Abramoff, M. D.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

Z. Hu, M. Niemeijer, K. Lee, M. D. Abramoff, M. Sonka, and M. K. Garvin, “Automated segmentation of the optic disc margin in 3-D optical coherence tomography images using a graph-theoretic approach,” Proc. SPIE 7262, 72620U (2009).
[Crossref]

Abràmoff, M. D.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[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. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

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. Imaging 27(10), 1495–1505 (2008).
[Crossref] [PubMed]

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. (Springer, 2014), pp. 739–746.
[Crossref]

Acharya U, R.

J. Nayak, R. Acharya U, P. S. Bhat, N. Shetty, and T. C. Lim, “Automated diagnosis of glaucoma using digital fundus images,” J. Med. Syst. 33(5), 337–346 (2009).
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Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
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Anderson, D. R.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, D. R. Anderson, and Cirrus Optical Coherence Tomography Normative Database Study Group, “Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes,” Ophthalmology 118(2), 241–248 (2011).
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Antony, B. J.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[Crossref]

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. (Springer, 2014), pp. 739–746.
[Crossref]

Aung, T.

Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis,” Ophthalmology 121(11), 2081–2090 (2014).
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R. N. Weinreb, T. Aung, and F. A. Medeiros, “The pathophysiology and treatment of glaucoma: a review,” JAMA 311(18), 1901–1911 (2014).
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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. Imaging 32(2), 376–386 (2013).
[Crossref] [PubMed]

Bellezza, A.

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Vis. Sci. 48(7), 3195–3208 (2007).
[Crossref] [PubMed]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Bhat, P. S.

J. Nayak, R. Acharya U, P. S. Bhat, N. Shetty, and T. C. Lim, “Automated diagnosis of glaucoma using digital fundus images,” J. Med. Syst. 33(5), 337–346 (2009).
[Crossref] [PubMed]

Bock, R.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

R. Bock, J. Meier, G. Michelson, L. G. Nyul, and J. Hornegger, “Classifying glaucoma with image-based features from fundus photographs,” in Joint Pattern Recognition Symposium. (Springer, 2007), pp. 355–364.
[Crossref]

Boulesteix, A. L.

C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: illustrations, sources and a solution,” BMC Bioinformatics 8(1), 25 (2007).
[Crossref] [PubMed]

Boykov, Y.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004).
[Crossref] [PubMed]

Bradski, G.

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 2564–2571.
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L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
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Broman, A. T.

H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol. 90(3), 262–267 (2006).
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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. Imaging 32(2), 376–386 (2013).
[Crossref] [PubMed]

Budenz, D. L.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, D. R. Anderson, and Cirrus Optical Coherence Tomography Normative Database Study Group, “Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes,” Ophthalmology 118(2), 241–248 (2011).
[Crossref] [PubMed]

Burgoyne, C. F.

B. C. Chauhan and C. F. Burgoyne, “From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change,” Am. J. Ophthalmol. 156(2), 218–227 (2013).
[Crossref] [PubMed]

A. S. Reis, N. O’Leary, H. Yang, G. P. Sharpe, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation Clinically Invisible Optic Disc Margin Anatomy,” Invest. Ophthalmol. Vis. Sci. 53(4), 1852–1860 (2012).
[Crossref] [PubMed]

N. G. Strouthidis, H. Yang, J. C. Downs, and C. F. Burgoyne, “Comparison of clinical and three-dimensional histomorphometric optic disc margin anatomy,” Invest. Ophthalmol. Vis. Sci. 50(5), 2165–2174 (2009).
[Crossref] [PubMed]

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Vis. Sci. 48(7), 3195–3208 (2007).
[Crossref] [PubMed]

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. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

Calabresi, P. A.

Calonder, M.

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “Brief: Binary robust independent elementary features,” Comput. Vis. ECCV. 2010, 778–792 (2010).

Caprioli, J.

G. J. Jaffe and J. Caprioli, “Optical coherence tomography to detect and manage retinal disease and glaucoma,” Am. J. Ophthalmol. 137(1), 156–169 (2004).
[Crossref] [PubMed]

Carass, A.

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Chakrabarty, L.

L. Chakrabarty, G. D. Joshi, A. Chakravarty, G. V. Raman, S. R. Krishnadas, and J. Sivaswamy, “Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs,” J. Glaucoma 25(7), 590–597 (2016).
[Crossref] [PubMed]

Chakravarthy, U.

G. Staurenghi, S. Sadda, U. Chakravarthy, R. F. Spaide, and International Nomenclature for Optical Coherence Tomography (IN•OCT) Panel, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
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Chakravarty, A.

L. Chakrabarty, G. D. Joshi, A. Chakravarty, G. V. Raman, S. R. Krishnadas, and J. Sivaswamy, “Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs,” J. Glaucoma 25(7), 590–597 (2016).
[Crossref] [PubMed]

Chauhan, B. C.

B. C. Chauhan and C. F. Burgoyne, “From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change,” Am. J. Ophthalmol. 156(2), 218–227 (2013).
[Crossref] [PubMed]

A. S. Reis, N. O’Leary, H. Yang, G. P. Sharpe, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation Clinically Invisible Optic Disc Margin Anatomy,” Invest. Ophthalmol. Vis. Sci. 53(4), 1852–1860 (2012).
[Crossref] [PubMed]

Chen, D. Z.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Chen, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

Chen, X.

I. Kopriva, F. Shi, and X. Chen, “Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography,” J. Biomed. Opt. 21(7), 076008 (2016).
[Crossref] [PubMed]

F. Shi, B. Tian, W. Zhu, D. Xiang, L. Zhou, H. Xu, and X. Chen, “Automated choroid segmentation in three-dimensional 1-μm wide-view OCT images with gradient and regional costs,” J. Biomed. Opt. 21(12), 126017 (2016).
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F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Cheng, C. Y.

Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

Chiu, S. J.

Cunefare, D.

De Dzanet, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Downs, J. C.

N. G. Strouthidis, H. Yang, J. C. Downs, and C. F. Burgoyne, “Comparison of clinical and three-dimensional histomorphometric optic disc margin anatomy,” Invest. Ophthalmol. Vis. Sci. 50(5), 2165–2174 (2009).
[Crossref] [PubMed]

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Vis. Sci. 48(7), 3195–3208 (2007).
[Crossref] [PubMed]

Dufour, P. A.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Fang, L.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Trans. Med. Imaging 36(2), 407–421 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[Crossref] [PubMed]

Farsiu, S.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Trans. Med. Imaging 36(2), 407–421 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[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. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

Flaxel, C. J.

Fua, P.

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “Brief: Binary robust independent elementary features,” Comput. Vis. ECCV. 2010, 778–792 (2010).

Fujimoto, J. G.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113(3), 325–332 (1995).
[Crossref] [PubMed]

Gao, E.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

Gao, S. S.

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. Imaging 32(2), 376–386 (2013).
[Crossref] [PubMed]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

Z. Hu, M. Niemeijer, K. Lee, M. D. Abramoff, M. Sonka, and M. K. Garvin, “Automated segmentation of the optic disc margin in 3-D optical coherence tomography images using a graph-theoretic approach,” Proc. SPIE 7262, 72620U (2009).
[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. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

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. Imaging 27(10), 1495–1505 (2008).
[Crossref] [PubMed]

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. (Springer, 2014), pp. 739–746.
[Crossref]

Girkin, C.

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Vis. Sci. 48(7), 3195–3208 (2007).
[Crossref] [PubMed]

Girkin, C. A.

Z. Hu, C. A. Girkin, A. Hariri, and S. R. Sadda, “Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information,” Proc. SPIE 9697, 96971S (2016).
[Crossref]

Gupta, P.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[Crossref]

Guymer, R. H.

Hariri, A.

Z. Hu, C. A. Girkin, A. Hariri, and S. R. Sadda, “Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information,” Proc. SPIE 9697, 96971S (2016).
[Crossref]

Hauser, M.

Hee, M. R.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113(3), 325–332 (1995).
[Crossref] [PubMed]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Hornegger, J.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

R. Bock, J. Meier, G. Michelson, L. G. Nyul, and J. Hornegger, “Classifying glaucoma with image-based features from fundus photographs,” in Joint Pattern Recognition Symposium. (Springer, 2007), pp. 355–364.
[Crossref]

Hothorn, T.

C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: illustrations, sources and a solution,” BMC Bioinformatics 8(1), 25 (2007).
[Crossref] [PubMed]

Hu, Z.

Z. Hu, C. A. Girkin, A. Hariri, and S. R. Sadda, “Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information,” Proc. SPIE 9697, 96971S (2016).
[Crossref]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. SPIE 7626, 76260U (2010).
[Crossref]

Z. Hu, M. Niemeijer, K. Lee, M. D. Abramoff, M. Sonka, and M. K. Garvin, “Automated segmentation of the optic disc margin in 3-D optical coherence tomography images using a graph-theoretic approach,” Proc. SPIE 7262, 72620U (2009).
[Crossref]

Huang, D.

Hwang, T. S.

Ishikawa, H.

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-D optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31(7), 1337–1345 (2012).
[Crossref] [PubMed]

Izatt, J. A.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[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. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113(3), 325–332 (1995).
[Crossref] [PubMed]

Jaffe, G. J.

G. J. Jaffe and J. Caprioli, “Optical coherence tomography to detect and manage retinal disease and glaucoma,” Am. J. Ophthalmol. 137(1), 156–169 (2004).
[Crossref] [PubMed]

Jia, Y.

Joshi, G. D.

L. Chakrabarty, G. D. Joshi, A. Chakravarty, G. V. Raman, S. R. Krishnadas, and J. Sivaswamy, “Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs,” J. Glaucoma 25(7), 590–597 (2016).
[Crossref] [PubMed]

Kagemann, L.

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-D optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31(7), 1337–1345 (2012).
[Crossref] [PubMed]

Kande, G. B.

P. S. Mittapalli and G. B. Kande, “Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma,” Biomed. Signal Process. Control 24, 34–46 (2016).
[Crossref]

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. Imaging 27(10), 1495–1505 (2008).
[Crossref] [PubMed]

Kolmogorov, V.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004).
[Crossref] [PubMed]

Konolige, K.

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 2564–2571.
[Crossref]

Kopriva, I.

I. Kopriva, F. Shi, and X. Chen, “Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography,” J. Biomed. Opt. 21(7), 076008 (2016).
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Figures (15)

Fig. 1
Fig. 1 One B-scan of ONH-centered SD-OCT image. (a) The normal case; (b) Glaucoma case.
Fig. 2
Fig. 2 ONH centered SD-OCT B-scan from a normal eye with 9 manually segmented surfaces defining 8 retinal layers.
Fig. 3
Fig. 3 Flowchart of the proposed method.
Fig. 4
Fig. 4 Graph construction for graph search with surface smoothness constraints (a) and surface separation constraints (b).
Fig. 5
Fig. 5 Graph construction for single surface shared-hole graph search in the x-z plane. The translucent yellow region indicates the shared-hole region for graph search. The yellow arcs are the hard constraint arcs.
Fig. 6
Fig. 6 Surface 1 segmentation results. The yellow and pink curves are the results of conventional graph search with small and big constant constraints, the red curve is the initialization result by Otsu thresholding with morphological operation, and the green curve shows the result of Otsu segmentation guided graph search with locally adaptive constraints.
Fig. 7
Fig. 7 The y-z image before (a) and after (b) B-scans alignment. The highlight box region indicates the distortion region.
Fig. 8
Fig. 8 The polar-transformation process. B-scan aligned volumetric image (a) is first interpolated in y direction into the same size as in x direction, making the x-y plane isotropic. Then, the center of x-y plane is regarded as the origin. Finally, inside the black circle area (b), 180 radial B-scans, 1 degree apart, is generated with 2D linear interpolation. (c) Three polar transformed radial B-scans on different angles.
Fig. 9
Fig. 9 41 feature maps extracted from a test data.
Fig. 10
Fig. 10 Sketch map for feature 3 and 4 in a radial B-scan. (a) Feature 3 calculated the total intensity difference between two 30 × 30 patches (yellow patch and green patch) to the left and right side of each voxel (red dot) along surface 9 (purple curve) in the radial B-scan. (b) Feature 4 is calculated as the total gradient difference 20 voxels (yellow curve and green curve) before and after each voxel (red dot) on surface 9 (purple curve).
Fig. 11
Fig. 11 Optic disc boundary detection results. Left: results showed on original B-scans, Right: results showed on radial B-scans.
Fig. 12
Fig. 12 The down-sampling process.
Fig. 13
Fig. 13 Permutation accuracy importance for 41 features.
Fig. 14
Fig. 14 An example of optic disc detection result. The first two rows show the detected disc region from glaucoma and normal data respectively, (a) (e) the reference standard, (b) (f) the result of the proposed method, (c) (g) the result of Hu’s method, (d) (h) the boundary overlay of reference standard, proposed method and Hu’s method. The third row shows a central B-scan from the glaucoma data as used in first row. (i) the reference standard, (j) the result of the proposed method, (k) the result of Hu’s method [25].
Fig. 15
Fig. 15 Example result of 9 surfaces segmentation on the B-scan next to the ONH region and on the center B-scan of ONH region. (a), (b) Segmented surfaces by conventional multi-resolution graph search with constant constraints [44]. (c), (d) Segmented surfaces by Zang’s method [30]. (e), (f) Segmented surfaces by OCTExplorer software [53]. (g), (h) Segmented surfaces by GS-AAM method [31]. (i), (j) Segmented surfaces by proposed method. (k), (l) Ground truth.

Tables (4)

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Table 1 Features for Optic Disc Region Classification

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Table 2 The DSC value of optic disc boundary detection (Mean ± SD)

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Table 3 Average Unsigned Border Positioning Error of ILM Surface Segmentation

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Table 4 Summary of Mean Unsigned Border Position Error for All Data

Equations (8)

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

w(x,y,z)={ c(x,y,z) z = 0 c(x,y,z)c(x,y,z1) otherwise
f up (x,y)= f Otsu (x,y)+dis t up
f down (x,y)= f Otsu (x,y)dis t down
Δ { ( x 1 , y 1 ),( x 1 +1, y 1 ) } x =| f Otsu ( x 1 , y 1 ) f Otsu ( x 1 +1, y 1 ) |+ d x
Δ { ( x 1 , y 1 ),( x 1 , y 1 +1 ) } y =| f Otsu ( x 1 , y 1 ) f Otsu ( x 1 , y 1 +1) |+ d y
Feature3= m=i30 i n=B(i)15 B(i)+15 I(m,n) m=i i+30 n=B(i)15 B(i)+15 I(m,n) i[30,M31]
Feature4= m=i i+20 G(m,B(m)) m=i20 i G(m,B(m)) i[20,M-21]
DSC(A,B)= 2(AB) (A+B)

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