Abstract

We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm’s capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.

© 2017 Optical Society of America

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2017 (7)

D. Hanumunthadu, J. P. Wang, W. Chen, E. N. Wong, Y. Chen, W. H. Morgan, P. J. Patel, and F. K. Chen, “Impact of retinal pigment epithelium pathology on spectral-domain optical coherence tomography-derived macular thickness and volume metrics and their intersession repeatability,” Clin. Exp. Ophthalmol. 45, 270–279 (2017).
[Crossref] [PubMed]

A. S. Willoughby, S. J. Chiu, R. K. Silverman, S. Farsiu, C. Bailey, H. E. Wiley, F. L. Ferris, and G. J. Jaffe, “Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software,” Translational Vision Science and Technology 6, 9 (2017).
[Crossref] [PubMed]

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).
[Crossref]

J. Oliveira, S. Pereira, L. Goncalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8, 281–297 (2017).
[Crossref] [PubMed]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “Joint retinal layer and fluid segmentation in oct scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8, 1874–1888 (2017).
[Crossref]

L. de Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. L. Rubin, “Automated intraretinal segmentation of sd-oct images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8, 1926–1949 (2017).
[Crossref]

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, 2732–2744 (2017).
[Crossref]

2016 (8)

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

A. M. Syed, T. Hassan, M. U. Akram, S. Naz, and S. Khalid, “Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3d retinal surfaces,” Computer Methods and Programs in Biomedicine 137, 1–10 (2016).
[Crossref]

M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sanchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imag. 35, 1273–1284 (2016).
[Crossref]

J. De Fauw, P. Keane, N. Tomasev, D. Visentin, G. van den Driessche, M. Johnson, C. O. Hughes, C. Chu, J. Ledsam, T. Back, T. Peto, G. Rees, H. Montgomery, R. Raine, O. Ronneberger, and J. Cornebise, “Automated analysis of retinal imaging using machine learning techniques for computer vision,” F1000Res 5, 1573 (2016).
[Crossref]

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive value of retinal morphology for visual acuity outcomes of different ranibizumab treatment regimens for neovascular AMD,” Ophthalmology 123, 60–69 (2016).
[Crossref]

S. Sharma, C. A. Toth, E. Daniel, J. E. Grunwald, M. G. Maguire, G.-S. Ying, J. Huang, D. F. Martin, G. J. Jaffe, and comparison of Age-related Macular Degeneration Treatments Trials Research Group, “Macular morphology and visual acuity in the second year of the comparison of age-related macular degeneration treatments trials,” Ophthalmology 123, 865–875 (2016).
[Crossref] [PubMed]

S. M. Waldstein, A.-M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-dimensionally quantified intraretinal and subretinal fluid with visual acuity in neovascular age-related macular degeneration,” JAMA Ophthalmology 134, 182–190 (2016).
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” Proc. SPIE 9784, 97840 (2016).

2015 (5)

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, e0133908 (2015).
[Crossref] [PubMed]

S. M. Waldstein, B. S. Gerendas, A. Montuoro, C. Simader, and U. Schmidt-Erfurth, “Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments,” Br. J. Ophthalmol. 99, 794–800 (2015).
[Crossref] [PubMed]

T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Information Processing in Medical Imaging 24, 437–448 (2015).
[PubMed]

J. Oliveira, S. Pereira, L. Goncalves, M. Ferreira, and C. A. Silva, “Sparse high order potentials for extending multi-surface segmentation of OCT images with drusen,” Conference Proceedings IEEE Engineering in Medicine Biology Society 2015, 2952–2955 (2015).

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

2014 (2)

2013 (4)

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

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
[Crossref]

J. Y. Lee, S. J. Chiu, P. P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, and G. J. Jaffe, “Fully automatic software for retinal thickness in eyes with diabetic macular edema from images acquired by cirrus and spectralis systems,” ÂăInvest. Ophthalmol. Vis. Sci. 54, 7595–7602 (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, 1133–1152 (2013).
[Crossref] [PubMed]

2012 (2)

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,” ÂăInvest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

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

2011 (6)

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. H. van de Ven, B. Kirchhof, C. B. Hoyng, B. Jeroen Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref]

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag. 30, 484–496 (2011).
[Crossref]

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” The Br. J. Ophthalmol. 95, 171–177 (2011).
[Crossref]

P. Malamos, C. Ahlers, G. Mylonas, C. Schutze, 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 (Philadelphia, Pa.) 31, 453–463 (2011).
[Crossref]

A. Wood, A. Binns, T. Margrain, W. Drexler, B. Povay, M. Esmaeelpour, and N. Sheen, “Retinal and choroidal thickness in early age-related macular degeneration,” Am. J. Ophthalmol. 152, 1030–1038 (2011).
[Crossref] [PubMed]

K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express 2, 1743–1756 (2011).
[Crossref] [PubMed]

2010 (4)

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
[Crossref]

S. Lu, C. Y. Cheung, J. Liu, J. H. Lim, C. K. Leung, and T. Y. Wong, “Automated layer segmentation of optical coherence tomography images,” IEEE Trans. Biomed. Eng. 57, 2605–2608 (2010).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
[Crossref] [PubMed]

Q. Yang, C. A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Express 18, 21293–21307 (2010).
[Crossref] [PubMed]

2009 (3)

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28, 1436–1447 (2009).
[Crossref]

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,” ÂăInvest. Ophthalmol. Vis. Sci. 50, 3378–3385 (2009).
[Crossref]

T. Fabritius, S. Makita, M. Miura, R. Myllyla, and Y. Yasuno, “Automated segmentation of the macula by optical coherence tomography,” Opt. Express 17, 15659–15669 (2009).
[Crossref] [PubMed]

2008 (1)

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imag. 27, 1495–1505 (2008).
[Crossref]

2007 (3)

M. Haeker, M. D. Abramoff, X. Wu, R. Kardon, and M. Sonka, “Use of varying constraints in optimal 3-D graph search for segmentation of macular optical coherence tomography images,” Medical Image Computing and Computer-Assisted Intervention 10, 244–251 (2007).
[PubMed]

A. E. Fung, G. A. Lalwani, P. J. Rosenfeld, S. R. Dubovy, S. Michels, W. J. Feuer, C. A. Puliafito, J. L. Davis, H. W. Flynn, and M. Esquiabro, “An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (lucentis) for neovascular age-related macular degeneration,” Am. J. Ophthalmol. 143, 566–583 (2007).
[Crossref]

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, and J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12, 041206 (2007).
[Crossref] [PubMed]

2006 (2)

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, 119–134 (2006).
[Crossref] [PubMed]

S. R. Sadda, Z. Wu, A. C. Walsh, L. Richine, J. Dougall, R. Cortez, and L. D. LaBree, “Errors in retinal thickness measurements obtained by optical coherence tomography,” Ophthalmology 113, 285–293 (2006).
[Crossref] [PubMed]

2001 (1)

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

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26, 297–302 (1945).
[Crossref]

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
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Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Medical Image Analysis 17, 907–928 (2013).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imag. 31, 1521–1531 (2012).
[Crossref]

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imag. 27, 1495–1505 (2008).
[Crossref]

M. Haeker, M. D. Abramoff, X. Wu, R. Kardon, and M. Sonka, “Use of varying constraints in optimal 3-D graph search for segmentation of macular optical coherence tomography images,” Medical Image Computing and Computer-Assisted Intervention 10, 244–251 (2007).
[PubMed]

Adrion, C.

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
[Crossref]

Ahlers, C.

P. Malamos, C. Ahlers, G. Mylonas, C. Schutze, 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 (Philadelphia, Pa.) 31, 453–463 (2011).
[Crossref]

Akram, M. U.

A. M. Syed, T. Hassan, M. U. Akram, S. Naz, and S. Khalid, “Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3d retinal surfaces,” Computer Methods and Programs in Biomedicine 137, 1–10 (2016).
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Alasil, 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,” ÂăInvest. Ophthalmol. Vis. Sci. 50, 3378–3385 (2009).
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Allingham, M. J.

Al-Louzi, O.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” Proc. SPIE 9784, 97840 (2016).

Antony, B. J.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” Proc. SPIE 9784, 97840 (2016).

Araie, M.

Arshavsky, V. Y.

Back, T.

J. De Fauw, P. Keane, N. Tomasev, D. Visentin, G. van den Driessche, M. Johnson, C. O. Hughes, C. Chu, J. Ledsam, T. Back, T. Peto, G. Rees, H. Montgomery, R. Raine, O. Ronneberger, and J. Cornebise, “Automated analysis of retinal imaging using machine learning techniques for computer vision,” F1000Res 5, 1573 (2016).
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Bailey, C.

A. S. Willoughby, S. J. Chiu, R. K. Silverman, S. Farsiu, C. Bailey, H. E. Wiley, F. L. Ferris, and G. J. Jaffe, “Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software,” Translational Vision Science and Technology 6, 9 (2017).
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Bengio, Y.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” International conference on artificial intelligence and statistics pp. 249–256 (2010).

Bi, H.

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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Binns, A.

A. Wood, A. Binns, T. Margrain, W. Drexler, B. Povay, M. Esmaeelpour, and N. Sheen, “Retinal and choroidal thickness in early age-related macular degeneration,” Am. J. Ophthalmol. 152, 1030–1038 (2011).
[Crossref] [PubMed]

Bogunovic, H.

Boyer, K.

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

Brinkmann, C. K.

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
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Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference9351, 234–241 (2015).

Burns, T. L.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28, 1436–1447 (2009).
[Crossref]

Calabresi, P. A.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” Proc. SPIE 9784, 97840 (2016).

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, 1133–1152 (2013).
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Caramoy, A.

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. H. van de Ven, B. Kirchhof, C. B. Hoyng, B. Jeroen Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Carass, A.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” Proc. SPIE 9784, 97840 (2016).

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

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
[Crossref]

Chakraborthi, D.

Chan, K.

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,” ÂăInvest. Ophthalmol. Vis. Sci. 50, 3378–3385 (2009).
[Crossref]

Charbel Issa, P.

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
[Crossref]

Chatterjee, J.

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, 119–134 (2006).
[Crossref] [PubMed]

Chen, F. K.

D. Hanumunthadu, J. P. Wang, W. Chen, E. N. Wong, Y. Chen, W. H. Morgan, P. J. Patel, and F. K. Chen, “Impact of retinal pigment epithelium pathology on spectral-domain optical coherence tomography-derived macular thickness and volume metrics and their intersession repeatability,” Clin. Exp. Ophthalmol. 45, 270–279 (2017).
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Chen, W.

D. Hanumunthadu, J. P. Wang, W. Chen, E. N. Wong, Y. Chen, W. H. Morgan, P. J. Patel, and F. K. Chen, “Impact of retinal pigment epithelium pathology on spectral-domain optical coherence tomography-derived macular thickness and volume metrics and their intersession repeatability,” Clin. Exp. Ophthalmol. 45, 270–279 (2017).
[Crossref] [PubMed]

Chen, X.

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

Chen, Y.

D. Hanumunthadu, J. P. Wang, W. Chen, E. N. Wong, Y. Chen, W. H. Morgan, P. J. Patel, and F. K. Chen, “Impact of retinal pigment epithelium pathology on spectral-domain optical coherence tomography-derived macular thickness and volume metrics and their intersession repeatability,” Clin. Exp. Ophthalmol. 45, 270–279 (2017).
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Cheung, C. Y.

S. Lu, C. Y. Cheung, J. Liu, J. H. Lim, C. K. Leung, and T. Y. Wong, “Automated layer segmentation of optical coherence tomography images,” IEEE Trans. Biomed. Eng. 57, 2605–2608 (2010).
[Crossref] [PubMed]

Chiu, S. J.

A. S. Willoughby, S. J. Chiu, R. K. Silverman, S. Farsiu, C. Bailey, H. E. Wiley, F. L. Ferris, and G. J. Jaffe, “Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software,” Translational Vision Science and Technology 6, 9 (2017).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

J. Y. Lee, S. J. Chiu, P. P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, and G. J. Jaffe, “Fully automatic software for retinal thickness in eyes with diabetic macular edema from images acquired by cirrus and spectralis systems,” ÂăInvest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
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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,” ÂăInvest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
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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, 19413–19428 (2010).
[Crossref] [PubMed]

Choi, S. S.

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, and J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12, 041206 (2007).
[Crossref] [PubMed]

Chu, C.

J. De Fauw, P. Keane, N. Tomasev, D. Visentin, G. van den Driessche, M. Johnson, C. O. Hughes, C. Chu, J. Ledsam, T. Back, T. Peto, G. Rees, H. Montgomery, R. Raine, O. Ronneberger, and J. Cornebise, “Automated analysis of retinal imaging using machine learning techniques for computer vision,” F1000Res 5, 1573 (2016).
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Collell, G.

G. Collell, D. Prelec, and K. Patil, “Reviving threshold-moving: a simple plug-in bagging ensemble for binary and multiclass imbalanced data,” arXiv e-prints abs/1606.08698 (2016).

Cornebise, J.

J. De Fauw, P. Keane, N. Tomasev, D. Visentin, G. van den Driessche, M. Johnson, C. O. Hughes, C. Chu, J. Ledsam, T. Back, T. Peto, G. Rees, H. Montgomery, R. Raine, O. Ronneberger, and J. Cornebise, “Automated analysis of retinal imaging using machine learning techniques for computer vision,” F1000Res 5, 1573 (2016).
[Crossref]

Cortez, R.

S. R. Sadda, Z. Wu, A. C. Walsh, L. Richine, J. Dougall, R. Cortez, and L. D. LaBree, “Errors in retinal thickness measurements obtained by optical coherence tomography,” Ophthalmology 113, 285–293 (2006).
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Cousins, S. W.

Cunefare, D.

Daniel, E.

S. Sharma, C. A. Toth, E. Daniel, J. E. Grunwald, M. G. Maguire, G.-S. Ying, J. Huang, D. F. Martin, G. J. Jaffe, and comparison of Age-related Macular Degeneration Treatments Trials Research Group, “Macular morphology and visual acuity in the second year of the comparison of age-related macular degeneration treatments trials,” Ophthalmology 123, 865–875 (2016).
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Davis, J. L.

A. E. Fung, G. A. Lalwani, P. J. Rosenfeld, S. R. Dubovy, S. Michels, W. J. Feuer, C. A. Puliafito, J. L. Davis, H. W. Flynn, and M. Esquiabro, “An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (lucentis) for neovascular age-related macular degeneration,” Am. J. Ophthalmol. 143, 566–583 (2007).
[Crossref]

de Boer, J. F.

de Sisternes, L.

Deak, G.

P. Malamos, C. Ahlers, G. Mylonas, C. Schutze, 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 (Philadelphia, Pa.) 31, 453–463 (2011).
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DeBuc, D. C.

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, e0133908 (2015).
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den Hollander, A. I.

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. H. van de Ven, B. Kirchhof, C. B. Hoyng, B. Jeroen Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref]

Dice, L. R.

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26, 297–302 (1945).
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S. Dieleman, J. Schluter, C. Raffel, E. Olson, S. K. Sonderby, D. Nouri, D. Maturana, and M. Thomas, “Lasagne: First release.” (2015).

Dougall, J.

S. R. Sadda, Z. Wu, A. C. Walsh, L. Richine, J. Dougall, R. Cortez, and L. D. LaBree, “Errors in retinal thickness measurements obtained by optical coherence tomography,” Ophthalmology 113, 285–293 (2006).
[Crossref] [PubMed]

Drexler, W.

A. Wood, A. Binns, T. Margrain, W. Drexler, B. Povay, M. Esmaeelpour, and N. Sheen, “Retinal and choroidal thickness in early age-related macular degeneration,” Am. J. Ophthalmol. 152, 1030–1038 (2011).
[Crossref] [PubMed]

Dubovy, S. R.

A. E. Fung, G. A. Lalwani, P. J. Rosenfeld, S. R. Dubovy, S. Michels, W. J. Feuer, C. A. Puliafito, J. L. Davis, H. W. Flynn, and M. Esquiabro, “An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (lucentis) for neovascular age-related macular degeneration,” Am. J. Ophthalmol. 143, 566–583 (2007).
[Crossref]

Dufour, P. A.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
[Crossref]

Dzanet, S. De

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
[Crossref]

Esmaeelpour, M.

A. Wood, A. Binns, T. Margrain, W. Drexler, B. Povay, M. Esmaeelpour, and N. Sheen, “Retinal and choroidal thickness in early age-related macular degeneration,” Am. J. Ophthalmol. 152, 1030–1038 (2011).
[Crossref] [PubMed]

Esquiabro, M.

A. E. Fung, G. A. Lalwani, P. J. Rosenfeld, S. R. Dubovy, S. Michels, W. J. Feuer, C. A. Puliafito, J. L. Davis, H. W. Flynn, and M. Esquiabro, “An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (lucentis) for neovascular age-related macular degeneration,” Am. J. Ophthalmol. 143, 566–583 (2007).
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Eter, N.

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
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Fabritius, T.

Fang, L.

Farsiu, S.

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, 2732–2744 (2017).
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A. S. Willoughby, S. J. Chiu, R. K. Silverman, S. Farsiu, C. Bailey, H. E. Wiley, F. L. Ferris, and G. J. Jaffe, “Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software,” Translational Vision Science and Technology 6, 9 (2017).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
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ÂaInvest. Ophthalmol. Vis. Sci. (5)

M. Fleckenstein, S. Schmitz-Valckenberg, C. Adrion, I. Kramer, N. Eter, H. M. Helb, C. K. Brinkmann, P. Charbel Issa, U. Mansmann, and F. G. Holz, “Tracking progression with spectral-domain optical coherence tomography in geographic atrophy caused by age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 51, 3846–3852 (2010).
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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,” ÂăInvest. Ophthalmol. Vis. Sci. 50, 3378–3385 (2009).
[Crossref]

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,” ÂăInvest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

J. Y. Lee, S. J. Chiu, P. P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, and G. J. Jaffe, “Fully automatic software for retinal thickness in eyes with diabetic macular edema from images acquired by cirrus and spectralis systems,” ÂăInvest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[Crossref]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. H. van de Ven, B. Kirchhof, C. B. Hoyng, B. Jeroen Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” ÂăInvest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Am. J. Ophthalmol. (2)

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A. Wood, A. Binns, T. Margrain, W. Drexler, B. Povay, M. Esmaeelpour, and N. Sheen, “Retinal and choroidal thickness in early age-related macular degeneration,” Am. J. Ophthalmol. 152, 1030–1038 (2011).
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Biomed. Opt. Express (9)

K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express 2, 1743–1756 (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, 1133–1152 (2013).
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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, 348–365 (2014).
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S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
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S. P. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomed. Opt. Express 7, 2888–2901 (2016).
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J. Oliveira, S. Pereira, L. Goncalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8, 281–297 (2017).
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A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “Joint retinal layer and fluid segmentation in oct scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8, 1874–1888 (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, 2732–2744 (2017).
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Br. J. Ophthalmol. (1)

S. M. Waldstein, B. S. Gerendas, A. Montuoro, C. Simader, and U. Schmidt-Erfurth, “Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments,” Br. J. Ophthalmol. 99, 794–800 (2015).
[Crossref] [PubMed]

Clin. Exp. Ophthalmol. (1)

D. Hanumunthadu, J. P. Wang, W. Chen, E. N. Wong, Y. Chen, W. H. Morgan, P. J. Patel, and F. K. Chen, “Impact of retinal pigment epithelium pathology on spectral-domain optical coherence tomography-derived macular thickness and volume metrics and their intersession repeatability,” Clin. Exp. Ophthalmol. 45, 270–279 (2017).
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Computer Methods and Programs in Biomedicine (1)

A. M. Syed, T. Hassan, M. U. Akram, S. Naz, and S. Khalid, “Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3d retinal surfaces,” Computer Methods and Programs in Biomedicine 137, 1–10 (2016).
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Conference Proceedings IEEE Engineering in Medicine Biology Society (1)

J. Oliveira, S. Pereira, L. Goncalves, M. Ferreira, and C. A. Silva, “Sparse high order potentials for extending multi-surface segmentation of OCT images with drusen,” Conference Proceedings IEEE Engineering in Medicine Biology Society 2015, 2952–2955 (2015).

Ecology (1)

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F1000Res (1)

J. De Fauw, P. Keane, N. Tomasev, D. Visentin, G. van den Driessche, M. Johnson, C. O. Hughes, C. Chu, J. Ledsam, T. Back, T. Peto, G. Rees, H. Montgomery, R. Raine, O. Ronneberger, and J. Cornebise, “Automated analysis of retinal imaging using machine learning techniques for computer vision,” F1000Res 5, 1573 (2016).
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IEEE Trans. Biomed. Eng. (1)

S. Lu, C. Y. Cheung, J. Liu, J. H. Lim, C. K. Leung, and T. Y. Wong, “Automated layer segmentation of optical coherence tomography images,” IEEE Trans. Biomed. Eng. 57, 2605–2608 (2010).
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D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imag. 20, 900–916 (2001).
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M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imag. 27, 1495–1505 (2008).
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M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sanchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imag. 35, 1273–1284 (2016).
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M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28, 1436–1447 (2009).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, 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. Imag. 32, 531–543 (2013).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imag. 31, 1521–1531 (2012).
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IEEE Trans. Pattern Anal. Mach. Intell. (1)

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, 119–134 (2006).
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Information Processing in Medical Imaging (1)

T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Information Processing in Medical Imaging 24, 437–448 (2015).
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J. Biomed. Opt. (1)

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, and J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12, 041206 (2007).
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JAMA Ophthalmology (1)

S. M. Waldstein, A.-M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-dimensionally quantified intraretinal and subretinal fluid with visual acuity in neovascular age-related macular degeneration,” JAMA Ophthalmology 134, 182–190 (2016).
[Crossref]

Medical Image Analysis (2)

F. Rathke, S. Schmidt, and C. Schnorr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Medical Image Analysis 18, 781–794 (2014).
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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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Ophthalmology (3)

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S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive value of retinal morphology for visual acuity outcomes of different ranibizumab treatment regimens for neovascular AMD,” Ophthalmology 123, 60–69 (2016).
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PLoS ONE (1)

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, e0133908 (2015).
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Proc. SPIE (1)

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Retina (Philadelphia, Pa.) (1)

P. Malamos, C. Ahlers, G. Mylonas, C. Schutze, 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 (Philadelphia, Pa.) 31, 453–463 (2011).
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W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” The Br. J. Ophthalmol. 95, 171–177 (2011).
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Translational Vision Science and Technology (1)

A. S. Willoughby, S. J. Chiu, R. K. Silverman, S. Farsiu, C. Bailey, H. E. Wiley, F. L. Ferris, and G. J. Jaffe, “Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software,” Translational Vision Science and Technology 6, 9 (2017).
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Figures (17)

Fig. 1
Fig. 1

The retina is defined as the region between the inner limiting membrane (ILM) and the outer boundary of the retinal pigment epithelium (RPE), including subretinal fluid but excluding retinal detachments. ILM and RPE are delineating in red on the OCT B-scan.

Fig. 2
Fig. 2

Generalized U-net architecture. The green and red boxes indicate the basic downsample and upsample units, respectively. The parameter c indicates the number of 3×3 convolutions in every unit. Making the network deeper by adding k more downsample units will increase the receptive field rk according to Eq. (5).

Fig. 3
Fig. 3

Original U-net architecture. The original U-net architecture is obtained by setting c = 2 and k = 4 in the generalized U-net architecture, i.e., 2 convolutional layers per downsample unit, and 4 downsample units in total.

Fig. 4
Fig. 4

Example of a training B-scan Bi (a) and its corresponding binary image Si (b) obtained from the provided manual annotations.

Fig. 5
Fig. 5

Example of (a) an input image, (b) the corresponding probability map produced by the proposed algorithm, and (c) the final thresholded output.

Fig. 6
Fig. 6

Qualitative assessment of segmentation performance (a) for all OCT volumes in the test set, (b) for OCT volumes with no pathology or signs of early AMD, (c) for OCT volumes with intermediate AMD, (d) for OCT volumes with advanced AMD.

Fig. 7
Fig. 7

Examples of B-scans showing the segmentation performance of the three different algorithms for the subgroup of early AMD: (a) Proposed algorithm, (b) Algorithm A, (c) Algorithm B. The segmentation boundaries are shown in red, while the manual annotation of A-scans intersecting with the 1 mm circle surrounding the fovea are shown in green.

Fig. 8
Fig. 8

Examples of B-scans showing the segmentation performance of the three different algorithms for the subgroup of intermediate AMD: (a) Proposed algorithm, (b) Algorithm A, (c) Algorithm B. The segmentation boundaries are shown in red, while the manual annotation of A-scans intersecting with the 1 mm circle surrounding the fovea are shown in green.

Fig. 9
Fig. 9

Examples of B-scans showing the segmentation performance of the three different algorithms for the subgroup of advanced AMD: (a) Proposed algorithm, (b) Algorithm A, (c) Algorithm B. The segmentation boundaries are shown in red, while the manual annotation of A-scans intersecting with the 1 mm circle surrounding the fovea are shown in green.

Fig. 10
Fig. 10

Example of a failed case for (a) the proposed method in the subgroup of intermediate AMD together with the segmentation produced by (b) Algorithm A and (c) Algorithm B for the same case. The segmentation boundaries are shown in red, while the manual annotation of A-scans intersecting with the 1 mm circle surrounding the fovea are shown in green.

Fig. 11
Fig. 11

Example of a failed case for (a) the proposed method in the subgroup of advanced AMD together with the segmentation produced by (b) Algorithm A and (c) Algorithm B for the same case. The segmentation boundaries are shown in red, while the manual annotation of A-scans intersecting with the 1 mm circle surrounding the fovea are shown in green.

Fig. 12
Fig. 12

Reliability analysis of the CMT measures obtained with (a) the proposed algorithm (b) Algorithm A and (c) Algorithm B, compared to the reference annotations. The different AMD subgroups are shown in a different color and symbol. The dashed line indicates no systematic difference.

Fig. 13
Fig. 13

Bland-Altman plot of the agreement between the manual central macular thickness (CMT) measures and the measures obtained with (a) the proposed algorithm, (b) Algorithm A and (c) Algorithm B. The different AMD subgroups are shown in a different color and symbol. The dotted lines indicate the bias and the 95% limits of agreement. The dashed line indicates the zero bias line.

Fig. 14
Fig. 14

Boxplots showing the distribution of the dice coefficient compared to the reference annotations (a) for all OCT volumes in the test set, (b) for OCT volumes with no pathology or signs of early AMD, (c) for OCT volumes with intermediate AMD, (d) for OCT volumes with advanced AMD.

Fig. 15
Fig. 15

Boxplots showing the distribution of the mean absolute distance from the ILM and RPE compared to the reference annotations (a) for all OCT volumes in the test set, (b) for OCT volumes with no pathology or signs of early AMD, (c) for OCT volumes with intermediate AMD, (d) for OCT volumes with advanced AMD.

Fig. 16
Fig. 16

Bland-Altman plot of the agreement between the manual central macular thickness (CMT) measures and the measures obtained with (a) the original U-net architecture and (b) the proposed generalized architecture. The different AMD subgroups are shown in a different color and symbol. The dotted lines indicate the bias and the 95% limits of agreement. The dashed line indicates the zero bias line.

Fig. 17
Fig. 17

Effect of increasing the receptive field. Top: Original U-net architecture with a receptive field of 140 × 140 pixels, Bottom: Proposed generalized U-net architecture with a receptive field of 572 × 572 pixels. The red lines delimits the retina segmentation output obtained with each architecture. Examples of the receptive field used by each architecture for two different locations (highlighted in yellow and green boxes) are included. The receptive field for the generalized architecture is the entire image.

Tables (1)

Tables Icon

Table 1 Mean central macular thickness (CMT) and various errors metrics derived from the retina segmentation obtained with the different algorithms and compared to the reference annotations for the entire test set and for OCT volumes with different AMD severity levels.

Equations (7)

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

M P C 3 R e L u C 3 R e L u
U C 2 R e L u C A T C 3 R e L u C 3 R e L u
r k = 2 ( r k 1 + 2 c )
r k = 2 k r 0 + 4 c j = 0 k 1 2 j = 2 k r 0 + 4 c ( 2 k 1 )
r k = 2 k ( 1 + 4 c ) 2 c
C = x B i , B i w i ( x ) log ( | S ˜ i ( x ) S i ( x ) | )
w i ( x ) = { N b a c k N r e t + N b a c k , if S i ( x ) = 1 , i . e . , x r e t i n a . N r e t N r e t + N b a c k , if S i ( x ) = 0 , i . e . , x b a c k g r o u n d .

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