Abstract

We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.

© 2017 Optical Society of America

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2017 (5)

2016 (15)

H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
[Crossref] [PubMed]

Z. Wu, D. Cunefare, E. Chiu, C. D. Luu, L. N. Ayton, C. A. Toth, S. Farsiu, and R. H. Guymer, “Longitudinal associations between microstructural changes and microperimetry in the early stages of age-related macular degeneration longitudinal structure and function associations in AMD,” Invest. Ophthalmol. Vis. Sci. 57(8), 3714–3722 (2016).
[Crossref] [PubMed]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

S. P. K. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomed. Opt. Express 7(7), 2888–2901 (2016).
[Crossref] [PubMed]

P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
[Crossref] [PubMed]

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in MRI images,” IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016).
[Crossref] [PubMed]

Qi Dou, Hao Chen, Lequan Yu, Lei Zhao, V. C. Jing Qin, Defeng Wang, Mok, Lin Shi, and Pheng-Ann Heng, “Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks,” IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016).
[Crossref] [PubMed]

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimers Dement (Amst) 4(8), 85–93 (2016).
[PubMed]

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(29187), 1–6 (2016).
[Crossref] [PubMed]

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
[Crossref] [PubMed]

2015 (4)

L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, “3-D adaptive sparsity based image compression with applications to optical coherence tomography,” IEEE Trans. Med. Imaging 34(6), 1306–1320 (2015).
[Crossref] [PubMed]

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

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61(10), 85–117 (2015).
[Crossref] [PubMed]

2014 (3)

2013 (3)

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

S. Bhat, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, “4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences,” IEEE Trans. Med. Imaging 32(3), 578–588 (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]

2012 (3)

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (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]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
[Crossref] [PubMed]

2011 (6)

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[Crossref] [PubMed]

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(9), 2493–2503 (2011).
[Crossref] [PubMed]

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

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2010 (3)

2009 (1)

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
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2006 (2)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
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2005 (2)

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
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M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005).
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2001 (1)

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1995 (1)

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
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1991 (1)

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1959 (1)

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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).
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B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
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Beaton, S.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
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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 Recognit. 44(8), 1590–1603 (2011).
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Bowes Rickman, C.

Boyer, K.

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
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Carass, A.

Carroll, J.

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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B. J. Lujan, A. Roorda, R. W. Knighton, and J. Carroll, “Revealing Henle’s Fiber Layer Using Spectral Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 52(3), 1486–1492 (2011).
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Cense, B.

Chakraborthi, D.

Chakraborty, D.

Chan, K.

Chan, R.

Chang, K. T.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
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Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Chen, L.-C.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected CRFs,” in Proceedings of International Conference on Learning Representation, (2015)

Chen, Q.

Chen, T.

Chen, X.

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).
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H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
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Z. Wu, D. Cunefare, E. Chiu, C. D. Luu, L. N. Ayton, C. A. Toth, S. Farsiu, and R. H. Guymer, “Longitudinal associations between microstructural changes and microperimetry in the early stages of age-related macular degeneration longitudinal structure and function associations in AMD,” Invest. Ophthalmol. Vis. Sci. 57(8), 3714–3722 (2016).
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S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
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S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
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S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
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F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
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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).
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Cole, E.

Comer, G. M.

Cooper, R. F.

Cousins, S. W.

Cunefare, D.

J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
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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).
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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
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D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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Z. Wu, D. Cunefare, E. Chiu, C. D. Luu, L. N. Ayton, C. A. Toth, S. Farsiu, and R. H. Guymer, “Longitudinal associations between microstructural changes and microperimetry in the early stages of age-related macular degeneration longitudinal structure and function associations in AMD,” Invest. Ophthalmol. Vis. Sci. 57(8), 3714–3722 (2016).
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de Sisternes, L.

Deak, G.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
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J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
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S. Bhat, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, “4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences,” IEEE Trans. Med. Imaging 32(3), 578–588 (2013).
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E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
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C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
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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).
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L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, “3-D adaptive sparsity based image compression with applications to optical coherence tomography,” IEEE Trans. Med. Imaging 34(6), 1306–1320 (2015).
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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).
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J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
[Crossref] [PubMed]

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
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B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimers Dement (Amst) 4(8), 85–93 (2016).
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J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(29187), 1–6 (2016).
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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

Z. Wu, D. Cunefare, E. Chiu, C. D. Luu, L. N. Ayton, C. A. Toth, S. Farsiu, and R. H. Guymer, “Longitudinal associations between microstructural changes and microperimetry in the early stages of age-related macular degeneration longitudinal structure and function associations in AMD,” Invest. Ophthalmol. Vis. Sci. 57(8), 3714–3722 (2016).
[Crossref] [PubMed]

L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, “3-D adaptive sparsity based image compression with applications to optical coherence tomography,” IEEE Trans. Med. Imaging 34(6), 1306–1320 (2015).
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P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
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P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
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S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
[Crossref] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[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]

Fawzi, A. A.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(29187), 1–6 (2016).
[Crossref] [PubMed]

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimers Dement (Amst) 4(8), 85–93 (2016).
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Feng, B.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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Ferreira, M.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
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Fujimoto, J. G.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
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C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
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[Crossref] [PubMed]

Gardner, T. W.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Garvin, M. K.

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 Recognit. 44(8), 1590–1603 (2011).
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Glybina, I. V.

K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary identification from optical coherence tomography images,” in IEEE Conference on Computational Intelligence in Bioinformatics and computational biology, (IEEE, 2015),1–8.
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Ying, H. S.

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S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected CRFs,” in Proceedings of International Conference on Learning Representation, (2015)

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W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, and C.-C. Loy, “Deepid-net: Deformable deep convolutional neural networks for object detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015),pp. 2403–2412.
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Zhang, H.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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Zhang, L.

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).
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Zhang, M.

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X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).

Zheng, S.

S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineety, Z. Su, D. Du, C. Huang, and P. H. S. Torr, “Conditional random fields as recurrent neural networks,” in Proceedings of International Conference on Computer Vision, (IEEE, 2015),pp. 1529–1537.
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X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).

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B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimers Dement (Amst) 4(8), 85–93 (2016).
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J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
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P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
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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. Express 1(5), 1358–1383 (2010).
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S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
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F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
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P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
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S. P. K. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomed. Opt. Express 7(7), 2888–2901 (2016).
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Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(9), 2493–2503 (2011).
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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).
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S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
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J. Oliveira, S. Pereira, L. Gonçalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8(1), 281–297 (2017).
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S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
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B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
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D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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IEEE Rev. Biomed. Eng. (1)

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3(1), 169–208 (2010).
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H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
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P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
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Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
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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).
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Figures (5)

Fig. 1
Fig. 1

Illustration of a retinal OCT image of a patient with non-exudative AMD with nine boundaries between the inner limiting membrane (ILM) labeled in blue and Bruch’s membrane (BrM) labeled in yellow. Eight layers consist of 1-2) nerve fiber layer (NFL); 2-3) ganglion cell layer and inner plexiform layer (GCL + IPL); 3-4) inner nuclear layer (INL); 4-5) outer plexiform layer (OPL); 5-6) outer nuclear layer (ONL); 6-7) inner segment (IS); 7-8) outer segment (OS); and 8-9) retinal pigment epithelium (RPE) and drusen complex (RPEDC).

Fig. 2
Fig. 2

Illustration of a typical CNN architecture with two convolution layers, two max pooling layers, one fully connected layer, and one soft max classification layer.

Fig. 3
Fig. 3

Outline of the proposed CNN-GS algorithm.

Fig. 4
Fig. 4

(a) A sample OCT B-scan from our data set, where a sample A-scan is delineated with red. Examples of zoomed in patches extracted from this A-scan are shown in (b). The vertical light-to-dark and dark-to-light gradient images for (a) used in the GTDP algorithm [23], normalized to values between 0 and 1, are shown in (c) and (d). The probability maps created by the CNN for each of the target nine layer boundaries on this B-scan are shown in (e)-(m). Different colors (from blue to red, as shown in the colorbar on the right) represent the probability values in these probability maps. The CNN classification results for test image (a) are shown in (n), where for each pixel, the assigned class corresponds to the class with the highest probability value.

Fig. 5
Fig. 5

Visual comparisons among the manual segmentations, OCTExplorer, DOCTRAP, and CNN-GS results on three non-exudative AMD images from the testing set.

Tables (2)

Tables Icon

Table 1 Architecture of the Cifar-CNN Used in Our Experiments

Tables Icon

Table 2 Differences (in pixels) in segmentation between manual grading and automated grading using our CNN-GS method, OCTExplorer software, and DOCTRAP software. The best results for each layer are labeled in bold. (*) emphasizes that OCTExplorer does not delineate the challenging Bruch’s membrane boundary and instead is targeted at segmenting the outer boundary of RPE, which in part explains the large differences with manual grading. (**) emphasizes that since manual grading was based on correcting the DOCTRAP results, there is a positive bias toward the reported DOCTRAP accuracy.

Equations (3)

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w ab =2( g a + g b )+ w min ,
( I X I min )/( I max I min ),
w ab,t Prob =2( P a,t + P b,tgb )+ w min ,

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