Abstract

We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 μm, with a mean (± SD) distance of 71 μm ± 107 μm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 μm ± 84 μm and 56 μm ± 80 μm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.

© 2017 Optical Society of America

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2017 (8)

G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

A. Govetto, R. A. Lalane, D. Sarraf, M. S. Figueroa, and J. P. Hubschman, “Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme,” Am. J. Ophthalmol. 175, 99 – 113 (2017).
[Crossref]

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

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, 579–592 (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] [PubMed]

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

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[Crossref] [PubMed]

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, 3627–3642 (2017).
[Crossref] [PubMed]

2016 (3)

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. Imag. 35, 1273–1284 (2016).
[Crossref]

C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
[Crossref] [PubMed]

S. M. Waldstein, A. 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]

2015 (1)

2014 (1)

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and AREDS 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, 162–172 (2014).
[Crossref]

2013 (4)

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

M. Adhi and J. S. Duker, “Optical coherence tomography–current and future applications,” Curr. Opin. Ophthalmol. 24, 213 (2013).
[Crossref] [PubMed]

P. C. Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. Heeren, T. Peto, F. G. Holz, and H. P. Scholl, “Macular telangiectasia type 2,” Progress in Retinal and Eye Research 34, 49–77 (2013).
[Crossref]

2012 (3)

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Molecular Vision 18, 2271–2278 (2012).
[PubMed]

B. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
[Crossref]

2011 (2)

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

2010 (4)

P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
[Crossref] [PubMed]

A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
[Crossref]

T. Otani, Y. Yamaguchi, and S. Kishi, “Correlation between visual acuity and foveal microstructural changes in diabetic macular edema,” Retina 30, 774–780 (2010).
[Crossref]

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]

2009 (1)

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[Crossref] [PubMed]

2008 (2)

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
[Crossref] [PubMed]

C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
[Crossref]

2007 (1)

Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography–measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114, 525–536 (2007).
[Crossref]

2006 (1)

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124, 193–198 (2006).
[Crossref] [PubMed]

2001 (2)

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12, 102–108 (2001).

Age-Related Eye Disease Study Research Group, “A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins c and e, beta carotene, and zinc for age-related macular degeneration and vision loss: Areds report no. 8,” Arch. Ophthalmol. 119, 1417–1436 (2001).
[Crossref] [PubMed]

1995 (1)

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

Abraham, P.

C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
[Crossref]

Abramoff, M. D.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015 (2015).
[Crossref] [PubMed]

Abràmoff, M. D.

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[Crossref] [PubMed]

Adhi, M.

M. Adhi and J. S. Duker, “Optical coherence tomography–current and future applications,” Curr. Opin. Ophthalmol. 24, 213 (2013).
[Crossref] [PubMed]

Ahn, S.

C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
[Crossref] [PubMed]

Allingham, M. J.

Bagherinia, H.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
[Crossref]

Balaratnasingam, C.

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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 AREDS 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, 162–172 (2014).
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A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
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A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124, 193–198 (2006).
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A. Govetto, R. A. Lalane, D. Sarraf, M. S. Figueroa, and J. P. Hubschman, “Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme,” Am. J. Ophthalmol. 175, 99 – 113 (2017).
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C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
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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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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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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).
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Jeroen Klevering, B.

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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Kafieh, R.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015 (2015).
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Katouzian, A.

Keane, P. A.

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
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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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Molecular Vision 18, 2271–2278 (2012).
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A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124, 193–198 (2006).
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 (2016).

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F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

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G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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A. Govetto, R. A. Lalane, D. Sarraf, M. S. Figueroa, and J. P. Hubschman, “Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme,” Am. J. Ophthalmol. 175, 99 – 113 (2017).
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B. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

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Langs, G.

S. M. Waldstein, A. 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).
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J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
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S. M. Waldstein, A. 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).
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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. Imag. 36, 407–421 (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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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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Li, X. T.

Liakopoulos, S.

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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
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B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

Lin, C. P.

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

G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in “IEEE Conf. Comput. Vis. Pattern Recognit.”, (2015), pp. 3431–3440.

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F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
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A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in “Proceedings of the International Machine Learning Society”, 30 (2013)

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A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12, 102–108 (2001).

McCann, J.

C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12, 102–108 (2001).

Mettu, P. S.

Mojana, F.

A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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Montuoro, A.

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] [PubMed]

B. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
[Crossref]

Morlet, J.

M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, “A real-time algorithm for signal analysis with the help of the wavelet transform,” in “Wavelets,” (Springer, 1990), pp. 286–297.
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P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
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Murphy, K.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 (2016).

Navab, N.

Ng, A. Y.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in “Proceedings of the International Machine Learning Society”, 30 (2013)

Nicholas, P.

Nie, Q.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
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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 AREDS 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, 162–172 (2014).
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Oster, S. F.

A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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T. Otani, Y. Yamaguchi, and S. Kishi, “Correlation between visual acuity and foveal microstructural changes in diabetic macular edema,” Retina 30, 774–780 (2010).
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 (2016).

Paques, M.

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12, 102–108 (2001).

Peto, T.

P. C. Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. Heeren, T. Peto, F. G. Holz, and H. P. Scholl, “Macular telangiectasia type 2,” Progress in Retinal and Eye Research 34, 49–77 (2013).
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Philip, A.

S. M. Waldstein, A. 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).
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Prince, J. L.

Puliafito, C. A.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
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R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015 (2015).
[Crossref] [PubMed]

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C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
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F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
[Crossref]

Rossant, F.

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

Roy, A. G.

Rubio, R. G.

P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
[Crossref] [PubMed]

Rundle, A. C.

P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
[Crossref] [PubMed]

Sadda, S. R.

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
[Crossref] [PubMed]

Sahel, J.-A.

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

Sánchez, C. I.

G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[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. Imag. 35, 1273–1284 (2016).
[Crossref]

B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

Saroj, N.

P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
[Crossref] [PubMed]

Sarraf, D.

A. Govetto, R. A. Lalane, D. Sarraf, M. S. Figueroa, and J. P. Hubschman, “Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme,” Am. J. Ophthalmol. 175, 99 – 113 (2017).
[Crossref]

Schmidt-Erfurth, U.

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] [PubMed]

S. M. Waldstein, A. 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. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
[Crossref]

Schmidt-Erfurth, U. M.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol. (2010).
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Schneider, S.

C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
[Crossref]

Scholl, H. P.

P. C. Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. Heeren, T. Peto, F. G. Holz, and H. P. Scholl, “Macular telangiectasia type 2,” Progress in Retinal and Eye Research 34, 49–77 (2013).
[Crossref]

Schuman, J. S.

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124, 193–198 (2006).
[Crossref] [PubMed]

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

Setio, A. A. A.

G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Shams, N.

C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
[Crossref]

Sheet, D.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in “IEEE Conf. Comput. Vis. Pattern Recognit.”, (2015), pp. 3431–3440.

Simader, C.

S. M. Waldstein, A. 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. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

Smailhodzic, D.

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Molecular Vision 18, 2271–2278 (2012).
[PubMed]

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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Sonka, M.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015 (2015).
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Sotirchos, E. S.

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385 (2015).

Swanson, E. A.

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

Tchamitchian, P.

M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, “A real-time algorithm for signal analysis with the help of the wavelet transform,” in “Wavelets,” (Springer, 1990), pp. 286–297.
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
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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. Imag. 35, 1273–1284 (2016).
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B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

Tick, S.

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and AREDS 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, 162–172 (2014).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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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).
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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Molecular Vision 18, 2271–2278 (2012).
[PubMed]

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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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

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G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[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. Imag. 35, 1273–1284 (2016).
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M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[Crossref] [PubMed]

B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

van Grinsven, M. J.

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[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. Imag. 35, 1273–1284 (2016).
[Crossref]

Venhuizen, F. G.

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[Crossref] [PubMed]

B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

Wachinger, C.

Waldstein, S.

B. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

Waldstein, S. M.

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] [PubMed]

S. M. Waldstein, A. 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]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
[Crossref]

Walsh, A. C.

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
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Wang, F.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
[Crossref]

Wang, M.

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
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M. J. Hogan and J. JA Weddell, “Histology of the human eye: an atlas and textbook,” (1971).

Wu, J.

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
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T. Otani, Y. Yamaguchi, and S. Kishi, “Correlation between visual acuity and foveal microstructural changes in diabetic macular edema,” Retina 30, 774–780 (2010).
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Yannuzzi, L. A.

C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (2016).
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Ying, H. S.

Yu, F.

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and AREDS 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, 162–172 (2014).
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C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
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Yuille, A. L.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 (2016).

Yuson, R. M.

A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385 (2015).

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

Am. J. of Ophthalmol. (1)

C. D. Regillo, D. M. Brown, P. Abraham, H. Yue, T. Ianchulev, S. Schneider, and N. Shams, “Randomized, double-masked, sham-controlled trial of ranibizumab for neovascular age-related macular degeneration: Pier study year 1,” Am. J. of Ophthalmol. 145, 239–248 (2008).
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Am. J. Ophthalmol. (2)

A. S. Maheshwary, S. F. Oster, R. M. Yuson, L. Cheng, F. Mojana, and W. R. Freeman, “The association between percent disruption of the photoreceptor inner segment–outer segment junction and visual acuity in diabetic macular edema,” Am. J. Ophthalmol. 150, 63–67 (2010).
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A. Govetto, R. A. Lalane, D. Sarraf, M. S. Figueroa, and J. P. Hubschman, “Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme,” Am. J. Ophthalmol. 175, 99 – 113 (2017).
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Arch. Ophthalmol. (3)

Age-Related Eye Disease Study Research Group, “A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins c and e, beta carotene, and zinc for age-related macular degeneration and vision loss: Areds report no. 8,” Arch. Ophthalmol. 119, 1417–1436 (2001).
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M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124, 193–198 (2006).
[Crossref] [PubMed]

Biomed. Opt. Express (7)

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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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]

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, 579–592 (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] [PubMed]

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

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[Crossref] [PubMed]

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, 3627–3642 (2017).
[Crossref] [PubMed]

Curr. Opin. Ophthalmol. (1)

M. Adhi and J. S. Duker, “Optical coherence tomography–current and future applications,” Curr. Opin. Ophthalmol. 24, 213 (2013).
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Eur. J. Ophthalmol. (1)

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12, 102–108 (2001).

IEEE Trans. Med. Imag. (3)

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. Imag. 35, 1273–1284 (2016).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imag. 32, 2034–2049, (2013).
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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. Imag. 36, 407–421 (2017).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (3)

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,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

B. Gerendas, S. Waldstein, J. Lammer, A. Montuoro, G. Bota, C. Simader, U. Schmidt-Erfurth, and Vienna Reading Center, “Centerpoint replotting and its effects on central retinal thickness in four prevalent SD-OCT devices,” Invest. Ophthalmol. Vis. Sci. 53, 4114 (2012).

S. Tick, F. Rossant, I. Ghorbel, A. Gaudric, J.-A. Sahel, P. Chaumet-Riffaud, and M. Paques, “Foveal shape and structure in a normal population,” Invest. Ophthalmol. Vis. Sci. 52, 5105–5110 (2011).
[Crossref] [PubMed]

JAMA Ophthalmology (1)

S. M. Waldstein, A. 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]

Med. Image Anal. (2)

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[Crossref] [PubMed]

G. Litjens, T. Kooi, E. B. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Molecular Vision (1)

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Molecular Vision 18, 2271–2278 (2012).
[PubMed]

Ophthalmic Surgery, Lasers and Imaging Retina (1)

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surgery, Lasers and Imaging Retina 43, S32–S37 (2012).
[Crossref]

Ophthalmology (5)

P. A. Keane, S. Liakopoulos, K. T. Chang, M. Wang, L. Dustin, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in neovascular age-related macular degeneration,” Ophthalmology 115, 2206–2214 (2008).
[Crossref] [PubMed]

P. A. Campochiaro, J. S. Heier, L. Feiner, S. Gray, N. Saroj, A. C. Rundle, W. Y. Murahashi, R. G. Rubio, and BRAVO Investigators, “Ranibizumab for macular edema following branch retinal vein occlusion: six-month primary end point results of a phase iii study,” Ophthalmology 117, 1102–1112 (2010).
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C. Balaratnasingam, M. Inoue, S. Ahn, J. McCann, E. Dhrami-Gavazi, L. A. Yannuzzi, and K. B. Freund, “Visual acuity is correlated with the area of the foveal avascular zone in diabetic retinopathy and retinal vein occlusion,” Ophthalmology 123, 2352–2367 (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 AREDS 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, 162–172 (2014).
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Opt. Express (1)

Progress in Retinal and Eye Research (1)

P. C. Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. Heeren, T. Peto, F. G. Holz, and H. P. Scholl, “Macular telangiectasia type 2,” Progress in Retinal and Eye Research 34, 49–77 (2013).
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Retina (1)

T. Otani, Y. Yamaguchi, and S. Kishi, “Correlation between visual acuity and foveal microstructural changes in diabetic macular edema,” Retina 30, 774–780 (2010).
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W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol. (2010).
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J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated fovea detection in spectral domain optical coherence tomography scans of exudative macular disease,” Int. J. of Biomed. Imaging2016 (2016).
[Crossref]

B. Liefers, F. G. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken, and C. I. Sánchez, “Fovea detection in optical coherence tomography using convolutional neural networks,” Proc. SPIE10133, (2017).

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 (2016).

M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, “A real-time algorithm for signal analysis with the help of the wavelet transform,” in “Wavelets,” (Springer, 1990), pp. 286–297.
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Theano Development Team, “Theano: A Python framework for fast computation of mathematical expressions,” arXiv e-prints abs/1605.02688 (2016).

S. Dieleman, J. Schlüter, C. Raffel, E. Olson, S. K. Sønderby, and D. Nouri, “Lasagne: First release,” http://dx.doi.org/10.5281/zenodo.27878 (2015).

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in “IEEE Conf. Comput. Vis. Pattern Recognit.”, (2015), pp. 3431–3440.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385 (2015).

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference 9351, 234–241 (2015).

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in “Proceedings of the International Machine Learning Society”, 30 (2013)

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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

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[Crossref] [PubMed]

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Figures (9)

Fig. 1
Fig. 1 Examples of a fovea as seen in scanning laser ophthalmoscopy (SLO, left column) and OCT (right column) in a healthy retina (top) and a pathological retina (bottom) where the presence of cysts affects the expected appearance of the fovea. The green lines on the SLO images show the position of the B-scans. The red line on the SLO indicates the B-scan with the foveal center (as annotated by the reference observer). The red line on the OCT indicates the lateral position of the fovea. Transversal and lateral are used to indicate directions in the en-face plane. Axial refers to the depth of the B-scan in the retina. The top image has been acquired with a dense scanning protocol (37 B-scans with a transversal resolution of 119 μm). The bottom image has been acquired with a sparse scanning protocol (19 B-scans with a transversal resolution of 257 μm). The images are cropped in the axial direction for visualization purposes.
Fig. 2
Fig. 2 Visualization of the network architecture. The solid arrows represent convolution operations. The dashed arrows represent a copy operation. The blue layers represent the output of the regular convolutions. The green layers represent the output of the dilated convolutions. The resolution of the feature maps remains the same throughout the network. Because the network is fully convolutional, it can be applied to inputs of arbitrary size.
Fig. 3
Fig. 3 Visualization of the dilated convolution filter at layer 3, with a receptive field of 11 × 9 pixels. The pixels with black dots are included in this filter. These pixels have a receptive field of 5 × 5 pixels each, as a result of the previous two convolution layers. The different shades in the figure represent the overlap of these subfields.
Fig. 4
Fig. 4 Preview of the likelihood maps as generated by the network. Top images show the input foveal B-scan. The bottom image includes a heatmap overlay representing the fovea likelihood. The left example represents an intermediate AMD case, whereas the right case represents advanced AMD.
Fig. 5
Fig. 5 Boxplots of the distance to the annotation per AMD severity level for the automatic method (M), the two observers (O1 and 02) and the scan center (SC). The errors (distance > 750 μm) are included for the creation of the boxplots, but are cut off from the figure for visualization purposes.
Fig. 6
Fig. 6 Examples of correct detections of the foveal center (images are cropped and do not represent the full extend of the B-scan). The red line indicates the reference location, the cyan circle represents the predicted fovea location by the method. The method performs well even in case of A: noisy or tilted images; B: large cysts; C: structural disruption due to fibrosis; D: absent or minor foveal depression; E: other uncategorized structural deformations. All errors in these images are smaller than 62 μm.
Fig. 7
Fig. 7 Example image where the reference observer probably overlooked the true fovea location and annotated a confounding location in the retina. The red line indicates the reference location and the cyan circle is the predicted location of the automatic method. The (overlapping) green lines on the OCT indicate the locations predicted by the two observers.
Fig. 8
Fig. 8 Example error where the retina is tilted and cut-off at the top. The diagonal cut of the retinal layers to the right of the predicted location resembles a confluence of layers as typically observed near the fovea. In conjunction with the true fovea appearing a bit obfuscated, this has led to a misclassification.
Fig. 9
Fig. 9 Example error where the true fovea is affected and lacks many of the typical characteristics. The structure in the inner retinal layers disappears around the confounding location that was selected by the method. Therefore this location can easily be confused with the foveal center.

Tables (5)

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Table 1 Summary of the network architecture. The dilation (di) refers to the spacing in the dilated filters in the horizontal and vertical direction, respectively. The receptive fields indicate the size of the contextual window of all pixels that can influence the network output at that layer.

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Table 2 Variations of the proposed network architecture.

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Table 3 Results for different variations of the proposed network architecture. Values in the columns indicate the number of detections within the specified distance category (in μm).

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Table 4 Results for the variations to the proposed training procedure. Number of updates refers to the number of iterations, or update operations (back-propagation) that were made during training. Training time refers to the total time needed to train the CNN.

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Table 5 Fovea detection performance reported by previous and current work. Accuracy (Acc.) refers to the number of detections within 750 μm of the reference annotation.

Equations (3)

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R i = R i 1 + d i ( k i 1 )
p i = w i j X w j
c = i B w i r i i B w i

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