Abstract

Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch’s membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (3)

2018 (2)

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
[Crossref]

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
[Crossref]

2017 (8)

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 (2017).
[Crossref]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref]

P. Zang, S. S. Gao, T. S. Hwang, C. J. Flaxel, D. J. Wilson, J. C. Morrison, D. Huang, D. Li, and Y. Jia, “Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography,” Biomed. Opt. Express 8(3), 1306 (2017).
[Crossref]

S. Li, D. Cunefare, C. Wang, R. H. Guymer, L. Fang, 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]

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
[Crossref]

S. Lee, M. Heisler, P. J. Mackenzie, M. V. Sarunic, and M. Faisal, “Quantifying variability in longitudinal peripapillary RNFL and choroidal layer thickness using surface based registration of OCT Images,” Transl. Vis. Sci. Technol. 6(1), 11–20 (2017).
[Crossref]

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
[Crossref]

2016 (1)

J. M. D. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of Bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Invest. Ophthalmol. Visual Sci. 57(9), OCT575 (2016).
[Crossref]

2015 (1)

C. Burgoyne, “The morphological difference between glaucoma and other optic neuropathies,” J. Neuro-Ophthalmology 35, S8–S21 (2015).
[Crossref]

2014 (3)

S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
[Crossref]

M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
[Crossref]

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]

2013 (3)

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]

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 (2013).
[Crossref]

S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
[Crossref]

2012 (1)

A. S. C. 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,” Invest. Ophthalmol. Visual Sci. 53(4), 1852–1860 (2012).
[Crossref]

2011 (1)

M. Young, S. Lee, M. F. Beg, P. J. Mackenzie, and M. V. Sarunic, “High speed morphometric imaging of the optic nerve head with 1 µm OCT,” Invest. Ophthalmol. Visual Sci. 52(9), 6720 (2011).
[Crossref]

2010 (1)

2009 (1)

Y. H. H. Kwon, J. H. H. Fingert, M. H. H. Kuehn, and W. L. M. L. M. Alward, “Primary open-angle glaucoma,” N. Engl. J. Med. 360(11), 1113–1124 (2009).
[Crossref]

2008 (2)

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]

D. S. Greenfield and R. N. Weinreb, “Role of optic nerve imaging in glaucoma clinical practice and clinical trials,” Am. J. Ophthalmol. 145(4), 598–603 (2008).
[Crossref]

2006 (1)

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]

Abràmoff, M. D.

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]

Albiani, D. a.

S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
[Crossref]

Alward, W. L. M. L. M.

Y. H. H. Kwon, J. H. H. Fingert, M. H. H. Kuehn, and W. L. M. L. M. Alward, “Primary open-angle glaucoma,” N. Engl. J. Med. 360(11), 1113–1124 (2009).
[Crossref]

Antony, B.

S. Sedai, B. Antony, D. Mahapatra, and R. Garnavi, “Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning,” in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Springer Verlag, 2018), Vol. 11039 LNCS, pp. 219–227.

Apostolopoulos, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual U-shape networks,” in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Springer Verlag, 2017), Vol. 10435 LNCS, pp. 294–301.

Aung, T.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
[Crossref]

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
[Crossref]

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]

S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
[Crossref]

Beg, M. F.

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
[Crossref]

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
[Crossref]

S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
[Crossref]

M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
[Crossref]

S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
[Crossref]

M. Young, S. Lee, M. F. Beg, P. J. Mackenzie, and M. V. Sarunic, “High speed morphometric imaging of the optic nerve head with 1 µm OCT,” Invest. Ophthalmol. Visual Sci. 52(9), 6720 (2011).
[Crossref]

M. Bhalla, M. Heisler, S. X. Han, M. V. Sarunic, M. F. Beg, P. J. Mackenzie, and S. Lee, “Longitudinal analysis of Bruch membrane opening morphometry in myopic glaucoma,” J. Glaucoma28(10), 889–895 (2019).
[Crossref]

S. Lee, M. F. Beg, and M. V. Sarunic, “Segmentation of the macular choroid in OCT images acquired at 830 nm and 1060 nm,” in Optical Coherence Tomography and Coherence Techniques VI (2013), Vol. 8802, p. 88020J.

Bhalla, M.

M. Bhalla, M. Heisler, S. X. Han, M. V. Sarunic, M. F. Beg, P. J. Mackenzie, and S. Lee, “Longitudinal analysis of Bruch membrane opening morphometry in myopic glaucoma,” J. Glaucoma28(10), 889–895 (2019).
[Crossref]

Bressler, N. M.

M. Pekala, N. Joshi, T. Y. A. Liu, N. M. Bressler, D. C. DeBuc, and P. Burlina, “Deep learning based retinal OCT segmentation,” Comput. Biol. Med. 114, 103445 (2019).
[Crossref]

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).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” CoRR abs/1505.0 (2015).

Burgoyne, C.

C. Burgoyne, “The morphological difference between glaucoma and other optic neuropathies,” J. Neuro-Ophthalmology 35, S8–S21 (2015).
[Crossref]

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]

A. S. C. 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,” Invest. Ophthalmol. Visual Sci. 53(4), 1852–1860 (2012).
[Crossref]

Burlina, P.

M. Pekala, N. Joshi, T. Y. A. Liu, N. M. Bressler, D. C. DeBuc, and P. Burlina, “Deep learning based retinal OCT segmentation,” Comput. Biol. Med. 114, 103445 (2019).
[Crossref]

Calabresi, P. A.

Carass, A.

Charlier, B.

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
[Crossref]

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
[Crossref]

Charon, N.

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S. Lee, M. Heisler, P. J. Mackenzie, M. V. Sarunic, and M. Faisal, “Quantifying variability in longitudinal peripapillary RNFL and choroidal layer thickness using surface based registration of OCT Images,” Transl. Vis. Sci. Technol. 6(1), 11–20 (2017).
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S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
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S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
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S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
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M. Bhalla, M. Heisler, S. X. Han, M. V. Sarunic, M. F. Beg, P. J. Mackenzie, and S. Lee, “Longitudinal analysis of Bruch membrane opening morphometry in myopic glaucoma,” J. Glaucoma28(10), 889–895 (2019).
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S. Lee, M. F. Beg, and M. V. Sarunic, “Segmentation of the macular choroid in OCT images acquired at 830 nm and 1060 nm,” in Optical Coherence Tomography and Coherence Techniques VI (2013), Vol. 8802, p. 88020J.

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Li, S.

Li, X.

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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Li, X. T.

Liu, L.

Liu, T. Y. A.

M. Pekala, N. Joshi, T. Y. A. Liu, N. M. Bressler, D. C. DeBuc, and P. Burlina, “Deep learning based retinal OCT segmentation,” Comput. Biol. Med. 114, 103445 (2019).
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Mackenzie, P. J.

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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S. Lee, M. Heisler, P. J. Mackenzie, M. V. Sarunic, and M. Faisal, “Quantifying variability in longitudinal peripapillary RNFL and choroidal layer thickness using surface based registration of OCT Images,” Transl. Vis. Sci. Technol. 6(1), 11–20 (2017).
[Crossref]

S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
[Crossref]

M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
[Crossref]

M. Young, S. Lee, M. F. Beg, P. J. Mackenzie, and M. V. Sarunic, “High speed morphometric imaging of the optic nerve head with 1 µm OCT,” Invest. Ophthalmol. Visual Sci. 52(9), 6720 (2011).
[Crossref]

M. Bhalla, M. Heisler, S. X. Han, M. V. Sarunic, M. F. Beg, P. J. Mackenzie, and S. Lee, “Longitudinal analysis of Bruch membrane opening morphometry in myopic glaucoma,” J. Glaucoma28(10), 889–895 (2019).
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J. M. D. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of Bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Invest. Ophthalmol. Visual Sci. 57(9), OCT575 (2016).
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S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
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Mark, J.

S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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Navab, N.

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Nicolela, M. T.

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A. S. C. 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,” Invest. Ophthalmol. Visual Sci. 53(4), 1852–1860 (2012).
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S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
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Pekala, M.

M. Pekala, N. Joshi, T. Y. A. Liu, N. M. Bressler, D. C. DeBuc, and P. Burlina, “Deep learning based retinal OCT segmentation,” Comput. Biol. Med. 114, 103445 (2019).
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Perera, S.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
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S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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Quigley, H. A.

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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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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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
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A. S. C. 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,” Invest. Ophthalmol. Visual Sci. 53(4), 1852–1860 (2012).
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
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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).
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S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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A. S. C. 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,” Invest. Ophthalmol. Visual Sci. 53(4), 1852–1860 (2012).
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Sreedhar, B. K.

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S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
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Subramanian, G.

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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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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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S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
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S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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Trouvé, A.

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
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S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
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S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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Wolf, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual U-shape networks,” in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Springer Verlag, 2017), Vol. 10435 LNCS, pp. 294–301.

Wong, T. 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).
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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).
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M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
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S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
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M. Young, S. Lee, M. F. Beg, P. J. Mackenzie, and M. V. Sarunic, “High speed morphometric imaging of the optic nerve head with 1 µm OCT,” Invest. Ophthalmol. Visual Sci. 52(9), 6720 (2011).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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Zhang, L.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
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S. K. Devalla, D. Pham, S. Kumar Panda, L. Zhang, G. Subramanian, A. Swaminathan, C. Z. Yun, M. Rajan, S. Mohan, R. Krishnadas, V. Senthil, J. Mark, S. De Leon, T. A. Tun, C.-Y. Cheng, L. Schmetterer, S. Perera, T. Aung, A. H. Thiéry, and M. J. A. Girard, “Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning,” (2017).
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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).
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D. S. Greenfield and R. N. Weinreb, “Role of optic nerve imaging in glaucoma clinical practice and clinical trials,” Am. J. Ophthalmol. 145(4), 598–603 (2008).
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Biomed. Opt. Express (8)

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 (2013).
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P. Zang, S. S. Gao, T. S. Hwang, C. J. Flaxel, D. J. Wilson, J. C. Morrison, D. Huang, D. Li, and Y. Jia, “Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography,” Biomed. Opt. Express 8(3), 1306 (2017).
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S. Li, D. Cunefare, C. Wang, R. H. Guymer, L. Fang, 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).
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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 (2017).
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A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
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S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
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P. Zang, J. Wang, T. T. Hormel, L. Liu, D. Huang, and Y. Jia, “Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search,” Biomed. Opt. Express 10(8), 4340 (2019).
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Y. He, A. Carass, Y. Liu, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT,” Biomed. Opt. Express 10(10), 5042 (2019).
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Br. J. Ophthalmol. (1)

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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Comput. Biol. Med. (1)

M. Pekala, N. Joshi, T. Y. A. Liu, N. M. Bressler, D. C. DeBuc, and P. Burlina, “Deep learning based retinal OCT segmentation,” Comput. Biol. Med. 114, 103445 (2019).
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Front. Neurosci. (1)

S. Lee, M. L. Heisler, K. Popuri, N. Charon, B. Charlier, A. Trouvé, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Age and glaucoma-related characteristics in retinal nerve fiber layer and choroid: Localized morphometrics and visualization using functional shapes registration,” Front. Neurosci. 11, 381 (2017).
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IEEE Trans. Med. Imaging (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).
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IEEE Trans. Pattern Anal. Mach. Intell. (1)

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
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Invest. Ophthalmol. Visual Sci. (6)

J. M. D. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of Bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Invest. Ophthalmol. Visual Sci. 57(9), OCT575 (2016).
[Crossref]

S. K. Devalla, K. S. Chin, J. M. Mari, T. A. Tun, N. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Visual Sci. 59, 63–74 (2018).
[Crossref]

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[Crossref]

S. Lee, N. Fallah, F. Forooghian, A. Ko, K. Pakzad-Vaezi, A. B. Merkur, A. W. Kirker, D. a. Albiani, M. Young, M. V. Sarunic, and M. F. Beg, “Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 54(4), 2864 (2013).
[Crossref]

S. Lee, S. X. Han, M. Young, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Optic nerve head and peripapillary morphometrics in myopic glaucoma,” Invest. Ophthalmol. Visual Sci. 55(7), 4378–4393 (2014).
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M. Young, S. Lee, M. Rateb, M. F. Beg, M. V. Sarunic, and P. J. Mackenzie, “Comparison of the clinical disc margin seen in stereo disc photographs with neural canal opening seen in optical coherence tomography images,” J. Glaucoma 23(6), 360–367 (2014).
[Crossref]

J. Neuro-Ophthalmology (1)

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[Crossref]

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S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
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Opt. Express (1)

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S. Lee, M. Heisler, P. J. Mackenzie, M. V. Sarunic, and M. Faisal, “Quantifying variability in longitudinal peripapillary RNFL and choroidal layer thickness using surface based registration of OCT Images,” Transl. Vis. Sci. Technol. 6(1), 11–20 (2017).
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[Crossref]

M. Bhalla, M. Heisler, S. X. Han, M. V. Sarunic, M. F. Beg, P. J. Mackenzie, and S. Lee, “Longitudinal analysis of Bruch membrane opening morphometry in myopic glaucoma,” J. Glaucoma28(10), 889–895 (2019).
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S. Lee, M. F. Beg, and M. V. Sarunic, “Segmentation of the macular choroid in OCT images acquired at 830 nm and 1060 nm,” in Optical Coherence Tomography and Coherence Techniques VI (2013), Vol. 8802, p. 88020J.

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Figures (9)

Fig. 1.
Fig. 1. Representative B scan (A) and its corresponding segmentations (B). The inner limiting membrane (magenta), posterior of the nerve fiber layer (yellow), Bruch's membrane (green), choroid-sclera boundary (cyan) and BMO points (red dots) are shown.
Fig. 2.
Fig. 2. Network architecture for adversarial layer segmentation of ONH peripapillary layers.
Fig. 3.
Fig. 3. High level overview of Faster-RCNN architecture used for BMO detection. The yellow boxes on the rightmost image represent the bounding boxes detected for the BMO, where the center of the box corresponds to the BMO.
Fig. 4.
Fig. 4. Examples of layer segmentation results using the different methods. The ILM (magenta), posterior border of the NFL (yellow), Bruch's membrane (green) and CS boundary (cyan) are shown for examples both in the peripapillary region and through the optic disc. The top two rows are from glaucomatous subject volumes and the bottom two rows are from healthy subject volumes. The black arrowhead in the Pix2Pix GAN image in the first row shows a segmentation error that does not occur in the corresponding semi-supervised image.
Fig. 5.
Fig. 5. Examples of BMO segmentations on control (A,B) and glaucomatous subjects (C,D,E). Ground truth labels are shown in green, with automated segmentations in yellow.
Fig. 6.
Fig. 6. Examples of manual and automated BMO segmentations. Manual segmentations (purple dots), the fit ellipse (green circle) and fit ellipse center (green star) as well as the automated segmentations (yellow dots), the fit ellipse (blue circle) and fit ellipse center (blue star) are shown for a control (A), high myope control (B) and glaucomatous (C) subject.
Fig. 7.
Fig. 7. Example images of the RNFL (A-C) and corresponding choroidal thickness (D-F) for a young control (A,D), myopic control (B,E) and glaucomatous (C,F) eye. Thickness measurements are inwardly bounded 0.25 mm from the best fit BMO ellipse and outwardly bounded at 1.25 mm from the best fit BMO ellipse.
Fig. 8.
Fig. 8. Scatter plots for the BMO area (left), mean RNFL thickness (middle) and mean choroid thickness (right).
Fig. 9.
Fig. 9. Bland-Altman Plots for the BMO area (left), mean RNFL thickness (middle) and mean choroid thickness (right).

Tables (3)

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Table 1. Dataset Demographics for the varying levels of segmentation

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Table 2. Mean DICE Coefficient for 57,319 B-scans before post-processing. The SS Pix2Pix GAN refers to the semi-supervised approach. Bolded values represent the best Dice Coefficient for that region out of all methods.

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Table 3. Mean values for the clinical parameters using both manual and automated methods.

Equations (2)

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D i c e ( X ) = 2 | X Y | | X | + | Y | ,
A P = n ( R n R n 1 ) P n ,