Abstract

Deep learning strategies, particularly convolutional neural networks (CNNs), are especially suited to finding patterns in images and using those patterns for image classification. The method is normally applied to an image patch and assigns a class weight to the patch; this method has recently been used to detect the probability of retinal boundary locations in OCT images, which is subsequently used to segment the OCT image using a graph-search approach. This paper examines the effects of a number of modifications to the CNN architecture with the aim of optimizing retinal layer segmentation, specifically the effect of patch size as well as the network architecture design on CNN performance and subsequent layer segmentation. The results demonstrate that increasing patch size can improve the performance of the classification and provides a more reliable segmentation in the analysis of retinal layer characteristics in OCT imaging. Similarly, this work shows that changing aspects of the CNN network design can also significantly improve the segmentation results. This work also demonstrates that the performance of the method can change depending on the number of classes (i.e. boundaries) used to train the CNN, with fewer classes showing an inferior performance due to the presence of similar image features between classes that can trigger false positives. Changes in the network (patch size and or architecture) can be applied to provide a superior segmentation performance, which is robust to the class effect. The findings from this work may inform future CNN development in OCT retinal image analysis.

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

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2017 (11)

J. F. de Boer, R. Leitgeb, and M. Wojtkowski, “Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT,” Biomed. Opt. Express 8(7), 3248–3280 (2017).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
[Crossref] [PubMed]

D. Sidibé, S. Sankar, G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, G. S. W. Tan, D. Milea, E. Lamoureux, T. Y. Wong, and F. Mériaudeau, “An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images,” Comput. Methods Programs Biomed. 139, 109–117 (2017).
[Crossref] [PubMed]

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review,” Transl. Vis. Sci. Technol. 6(4), 10 (2017).
[Crossref] [PubMed]

B. Liefers, F. G. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen, and C. I. Sánchez, “Automatic detection of the foveal center in optical coherence tomography,” Biomed. Opt. Express 8(11), 5160–5178 (2017).
[Crossref] [PubMed]

Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8(9), 4061–4076 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. 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(7), 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(8), 3627–3642 (2017).
[Crossref] [PubMed]

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

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

2016 (5)

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

B. Hassan, G. Raja, T. Hassan, and M. Usman Akram, “Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images,” J. Opt. Soc. Am. A 33(4), 455–463 (2016).
[Crossref] [PubMed]

P. Jin, H. Zou, J. Zhu, X. Xu, J. Jin, T. C. Chang, L. Lu, H. Yuan, S. Sun, B. Yan, J. He, M. Wang, and X. He, “Choroidal and retinal thickness in children with different refractive status measured by swept-source optical coherence tomography,” Am. J. Ophthalmol. 168, 164–176 (2016).
[Crossref] [PubMed]

D. Alonso-Caneiro, S. A. Read, S. J. Vincent, M. J. Collins, and M. Wojtkowski, “Tissue thickness calculation in ocular optical coherence tomography,” Biomed. Opt. Express 7(2), 629–645 (2016).
[Crossref] [PubMed]

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

2015 (4)

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5(4), 603–617 (2015).
[PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

2014 (2)

2013 (3)

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

S. Y. Park, S. M. Kim, Y.-M. Song, J. Sung, and D.-I. Ham, “Retinal thickness and volume measured with enhanced depth imaging optical coherence tomography,” Am. J. Ophthalmol. 156(3), 557–566 (2013).
[Crossref] [PubMed]

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013).
[Crossref] [PubMed]

2012 (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

2011 (2)

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(6), 1743–1756 (2011).
[Crossref] [PubMed]

G. Virgili, F. Menchini, V. Murro, E. Peluso, F. Rosa, and G. Casazza, “Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy,” Cochrane Database Syst. Rev. 7(7), CD008081 (2011).
[PubMed]

2010 (2)

2009 (2)

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728 (2009).
[Crossref] [PubMed]

2008 (1)

R. F. Spaide, H. Koizumi, and M. C. Pozzoni, “Enhanced depth imaging spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 146(4), 496–500 (2008).
[Crossref] [PubMed]

2007 (1)

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(4), 566–583 (2007).
[Crossref] [PubMed]

2005 (1)

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[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. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

2000 (1)

1999 (1)

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–105 (1999).
[Crossref] [PubMed]

1991 (1)

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

1959 (1)

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
[Crossref]

Abràmoff, M. D.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

Alonso-Caneiro, D.

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

D. Alonso-Caneiro, S. A. Read, S. J. Vincent, M. J. Collins, and M. Wojtkowski, “Tissue thickness calculation in ocular optical coherence tomography,” Biomed. Opt. Express 7(2), 629–645 (2016).
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S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
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M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
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Clausi, D. A.

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S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
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Conjeti, S.

Cooper, R. F.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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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(4), 566–583 (2007).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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C. Farabet, C. Couprie, L. Najman, and Y. Lecun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013).
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M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review,” Transl. Vis. Sci. Technol. 6(4), 10 (2017).
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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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(4), 566–583 (2007).
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H. Fu, Y. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 132–139 (2016).
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H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
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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(4), 566–583 (2007).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Ham, D.-I.

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

W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
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P. Jin, H. Zou, J. Zhu, X. Xu, J. Jin, T. C. Chang, L. Lu, H. Yuan, S. Sun, B. Yan, J. He, M. Wang, and X. He, “Choroidal and retinal thickness in children with different refractive status measured by swept-source optical coherence tomography,” Am. J. Ophthalmol. 168, 164–176 (2016).
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P. Jin, H. Zou, J. Zhu, X. Xu, J. Jin, T. C. Chang, L. Lu, H. Yuan, S. Sun, B. Yan, J. He, M. Wang, and X. He, “Choroidal and retinal thickness in children with different refractive status measured by swept-source optical coherence tomography,” Am. J. Ophthalmol. 168, 164–176 (2016).
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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
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H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
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Kim, S. H.

W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
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S. Y. Park, S. M. Kim, Y.-M. Song, J. Sung, and D.-I. Ham, “Retinal thickness and volume measured with enhanced depth imaging optical coherence tomography,” Am. J. Ophthalmol. 156(3), 557–566 (2013).
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W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
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W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

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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(4), 566–583 (2007).
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D. Sidibé, S. Sankar, G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, G. S. W. Tan, D. Milea, E. Lamoureux, T. Y. Wong, and F. Mériaudeau, “An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images,” Comput. Methods Programs Biomed. 139, 109–117 (2017).
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Lecun, Y.

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013).
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W. J. Lee, Y. K. Kim, Y. W. Kim, J. W. Jeoung, S. H. Kim, J. W. Heo, H. G. Yu, and K. H. Park, “Rate of macular ganglion cell-inner plexiform layer thinning in glaucomatous eyes with vascular endothelial growth factor inhibition,” J. Glaucoma 26(11), 980–986 (2017).
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H. Fu, Y. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 132–139 (2016).
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Liu, J.

H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
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H. Fu, Y. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 132–139 (2016).
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440 (2015).

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H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016).
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P. Jin, H. Zou, J. Zhu, X. Xu, J. Jin, T. C. Chang, L. Lu, H. Yuan, S. Sun, B. Yan, J. He, M. Wang, and X. He, “Choroidal and retinal thickness in children with different refractive status measured by swept-source optical coherence tomography,” Am. J. Ophthalmol. 168, 164–176 (2016).
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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
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Adv. Neural Inf. Process. Syst. (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

Am. J. Ophthalmol. (4)

P. Jin, H. Zou, J. Zhu, X. Xu, J. Jin, T. C. Chang, L. Lu, H. Yuan, S. Sun, B. Yan, J. He, M. Wang, and X. He, “Choroidal and retinal thickness in children with different refractive status measured by swept-source optical coherence tomography,” Am. J. Ophthalmol. 168, 164–176 (2016).
[Crossref] [PubMed]

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Biomed. Opt. Express (10)

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(6), 1743–1756 (2011).
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Figures (8)

Fig. 1
Fig. 1 Example of the spectral domain OCT B-scan captured using the instrument’s high resolution scanning protocol including (A) the fundus image and (B) B-scan. The B-scan is shown with the seven boundaries of interest (C), including the retinal pigment epithelium (RPE), inner segment ellipsoid (ISe), external limiting membrane (ELM), outer plexiform layer/inner nuclear layer (OPL/INL) boundary, inner nuclear layer/inner plexiform layer (INL/IPL) boundary, ganglion cell layer/nerve fiber layer (GCL/NFL) boundary and the inner limiting membrane (ILM). Insets show a zoomed version of the B-scan (D) without and (E) with the boundaries of interest.
Fig. 2
Fig. 2 Overview of the proposed method with the two major steps involved in the process. In the training step (top section) the different proposed networks can be substituted to evaluate the effect of different networks on the performance of the segmentation task.
Fig. 3
Fig. 3 Example OCT image with the layer of interest delineated in red (left) and the equivalent probability map for that layer (right), the last image displays the background (no boundary) class. For computational reasons the probability maps (values of one indicate high likelihood of the boundary to be present) are only computed a number of pixels equal to half the patch size above the ILM and below the RPE. The trained CNN CIFAR with a patch size of 65x65 was used to extract the seven probability maps associated with the boundaries of interest and the background class in this figure.
Fig. 4
Fig. 4 Mean absolute error difference in boundary position between the different network architectures and the manual observer for the entire data set.
Fig. 5
Fig. 5 Probability profile for the different considered networks for an individual A-scan in a representative OCT B-Scan (top left). Red line in OCT B-Scan represents the A-scan location. Solid circles indicate the true boundary position, whereas the lines show the probability of the boundary location predicted by the different CNN networks, including the CIFAR 33x33 (top right), the Complex 33x33 (bottom left) and the Complex 65x65 (bottom right).
Fig. 6
Fig. 6 Five B-scans from five different subjects are shown with the derived boundaries of interest. The B-scans represent a range of different examples (i.e. with typical variations in overall retinal thickness, thickness profiles and radial scan location). The segmentation based on the manual analysis (yellow) and the automatic analysis (red) is illustrated. The large Complex network (65x65) was used to generate the automatic results.
Fig. 7
Fig. 7 Example B-scan (A) showing the probability maps for three layers of interest (F), (ILM in blue, INL/IPL in orange and RPE in red). Each color indicates a high probability of a boundary to be present in that location. The images show the effect on performance while training the CNN with different networks (B-G is the CIFAR CNN 33x33, C-H is the Complex CNN 33x33, D-I is the CIFAR CNN 65x65, and E-J is the Complex CNN 65x65) and different numbers of training classes (B-C-D-E 4 classes and G-H-I-J 8 classes).
Fig. 8
Fig. 8 Example of four B-scans with disagreement between the manual (red line) and automatic methods (yellow line), due to the presence of retinal blood vessels within the layer in these locations and the associated changes in the intensity of the boundary. The bottom panel presents a close-up of the region of interest, with the corresponding region of the B-scan (I), the manual and automatic segmentation (II) and probability map for the GCL/NFL (III).

Tables (4)

Tables Icon

Table 1 Architecture of the four different CNNs used in this work. For comparison, the baseline network (33x33 CIFAR) proposed by Fang et al. [34] was included. The CIFAR 65x65 CNN extends the original input size to take advantage of the distribution of the information within the OCT image. The complex CNN network explores the benefit of altering the network architecture. The number in round brackets “()” indicates the number of filters, number in angle brackets “<>” indicates stride, and number in curly brackets “{}” indicates padding. Fully Con indicates a Fully Connected layer. For the 4 class network (section 3.3) the final layer will be “1x1 Fully Connected (4) <1> {0}”.

Tables Icon

Table 2 Difference in boundary position between the different network architectures and patch sizes and the manual observer for the entire data set (120 B-scans). The results are reported in mean values and (standard deviation) in pixel units.

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Table 3 Thickness repeatability for the baseline (33x33 CIFAR) and large Complex (65x65 Complex) network architectures considered in the study, tested on a data set with 84 pairs of B-scans. The results are reported in mean values and (standard deviation) in pixel units. Thickness definition; total retinal (TR) thickness [RPE to ILM], the RPE + ISe thickness [RPE to ISe], the inner segment (IS) thickness [ISe to ELM] and the ONL + OPL thickness [ELM to OPL/INL], INL thickness [OPL/INL to INL/IPL], IPL + GCL thickness [INL/IPL to GCL/NFL] and NFL thickness [GCL/NFL to ILM].

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Table 4 Difference in boundary position between the different network architectures and the manual observer for the entire data set (120 B-scans) with differing numbers of classes used during the training. The results are reported in mean values and (standard deviation) in pixel units.

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