Abstract

Accurate choroidal vessel segmentation with swept-source optical coherence tomography (SS-OCT) images provide unprecedented quantitative analysis towards the understanding of choroid-related diseases. Motivated by the leading segmentation performance in medical images from the use of deep learning methods, in this study, we proposed the adoption of a deep learning method, RefineNet, to segment the choroidal vessels from SS-OCT images. We quantitatively evaluated the RefineNet on 40 SS-OCT images consisting of ~3,900 manually annotated choroidal vessels regions. We achieved a segmentation agreement (SA) of 0.840 ± 0.035 with clinician 1 (C1) and 0.823 ± 0.027 with clinician 2 (C2). These results were higher than inter-observer variability measure in SA between C1 and C2 of 0.821 ± 0.037. Our results demonstrated that the choroidal vessels from SS-OCT can be automatically segmented using a deep learning method and thus provided a new approach towards an objective and reproducible quantitative analysis of vessel regions.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (3)

2017 (8)

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

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

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]

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]

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

2016 (2)

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

F. Lavinsky and D. Lavinsky, “Novel perspectives on swept-source optical coherence tomography,” Int. J. Retina Vitreous 2(1), 25 (2016).
[Crossref] [PubMed]

2013 (2)

2012 (2)

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

2011 (1)

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

2010 (1)

D. L. Nickla and J. Wallman, “The multifunctional choroid,” Prog. Retin. Eye Res. 29(2), 144–168 (2010).
[Crossref] [PubMed]

1986 (1)

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 1(8476), 307–310 (1986).
[Crossref] [PubMed]

Abràmoff, M. D.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

Ahn, E.

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

Altman, D. G.

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 1(8476), 307–310 (1986).
[Crossref] [PubMed]

Belongie, S.

A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,” Adv. Neural Inf. Process. Syst. (2016).

Ben Ayed, I.

J. Dolz, C. Desrosiers, and I. Ben Ayed, “3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study,” Neuroimage 170, 456–470 (2018).
[Crossref] [PubMed]

Bi, H.

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

Bi, L.

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

Binder, S.

Bland, J. M.

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 1(8476), 307–310 (1986).
[Crossref] [PubMed]

Chatfield, K.

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the Devil in the Details: Delving Deep into Convolutional Nets,” Comput. Sci. (2014).

Chen, H.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Chu, Z.

Cunefare, D.

Dai, Y.

Darrell, T.

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

Desrosiers, C.

J. Dolz, C. Desrosiers, and I. Ben Ayed, “3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study,” Neuroimage 170, 456–470 (2018).
[Crossref] [PubMed]

Dolz, J.

J. Dolz, C. Desrosiers, and I. Ben Ayed, “3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study,” Neuroimage 170, 456–470 (2018).
[Crossref] [PubMed]

Dou, Q.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Drexler, W.

Duan, L.

Esmaeelpour, M.

Faidley, E. A.

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

Feng, D.

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

Feng, D. D.

Fujimoto, J. G.

Fulham, M.

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

Gao, M.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Glittenberg, C.

Gregori, G.

Guymer, R. H.

Heng, P. A.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

Honegger, J.

Hong, Y. J.

Hoyng, C.

Huang, J.

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

Jirarattanasopa, P.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

Johnson, M. N.

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

Kajic, V.

Kim, J.

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]

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

Kraus, M. F.

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

Kumar, A.

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

Lavinsky, D.

F. Lavinsky and D. Lavinsky, “Novel perspectives on swept-source optical coherence tomography,” Int. J. Retina Vitreous 2(1), 25 (2016).
[Crossref] [PubMed]

Lavinsky, F.

F. Lavinsky and D. Lavinsky, “Novel perspectives on swept-source optical coherence tomography,” Int. J. Retina Vitreous 2(1), 25 (2016).
[Crossref] [PubMed]

Lee, K.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

Lenc, K.

A. Vedaldi and K. Lenc, “MatConvNet: Convolutional Neural Networks for MATLAB,” in ACM International Conference on Multimedia, 2015), 689–692.

Li, C.

Li, S.

Liefers, B.

Long, J.

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

Lu, L.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Lyndon, D.

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

Mollura, D.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Mullins, R. F.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

Nakata, I.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

Nickla, D. L.

D. L. Nickla and J. Wallman, “The multifunctional choroid,” Prog. Retin. Eye Res. 29(2), 144–168 (2010).
[Crossref] [PubMed]

Niemeijer, M.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

Nogues, I.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Oishi, A.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

Ooto, S.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

Othara, R.

Pan, X.

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

Qi, X.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Qin, J.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Rosenfeld, P. J.

Roth, H. R.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Sánchez, C. I.

Shelhamer, E.

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
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Shin, H. C.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Simonyan, K.

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the Devil in the Details: Delving Deep into Convolutional Nets,” Comput. Sci. (2014).

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Comput. Sci. (2014).

Skeie, J. M.

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

Sonka, M.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

Su, L.

Sui, X.

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

Summers, R. M.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

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Tsujikawa, A.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

van Ginneken, B.

van Grinsven, M. J. J. P.

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K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the Devil in the Details: Delving Deep into Convolutional Nets,” Comput. Sci. (2014).

A. Vedaldi and K. Lenc, “MatConvNet: Convolutional Neural Networks for MATLAB,” in ACM International Conference on Multimedia, 2015), 689–692.

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A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,” Adv. Neural Inf. Process. Syst. (2016).

Venhuizen, F. G.

Wallman, J.

D. L. Nickla and J. Wallman, “The multifunctional choroid,” Prog. Retin. Eye Res. 29(2), 144–168 (2010).
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Wang, R. K.

Wang, X.

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X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from OpticalCoherence Tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
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A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,” Adv. Neural Inf. Process. Syst. (2016).

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

Xu, Z.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
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P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
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Yan, K.

Yao, J.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Yasuno, Y.

Yin, Y.

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

Yoshimura, N.

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
[Crossref] [PubMed]

Yu, L.

H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Yu, S.

Zhang, L.

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
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Zhang, Q.

Zhang, S.

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

Zheng, Y.

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

Zhou, H.

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K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Comput. Sci. (2014).

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the Devil in the Details: Delving Deep into Convolutional Nets,” Comput. Sci. (2014).

Biomed. Opt. Express (6)

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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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).
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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).
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H. Zhou, Z. Chu, Q. Zhang, Y. Dai, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Attenuation correction assisted automatic segmentation for assessing choroidal thickness and vasculature with swept-source OCT,” Biomed. Opt. Express 9(12), 6067–6080 (2018).
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H. Zhou, Z. Chu, Q. Zhang, Y. Dai, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Attenuation correction assisted automatic segmentation for assessing choroidal thickness and vasculature with swept-source OCT,” Biomed. Opt. Express 9(12), 6067–6080 (2018).
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V. Kajić, M. Esmaeelpour, C. Glittenberg, M. F. Kraus, J. Honegger, R. Othara, S. Binder, J. G. Fujimoto, and W. Drexler, “Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data,” Biomed. Opt. Express 4(1), 134–150 (2013).
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IEEE J. Biomed. Health Inform. (1)

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (1)

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

Int. J. Retina Vitreous (1)

F. Lavinsky and D. Lavinsky, “Novel perspectives on swept-source optical coherence tomography,” Int. J. Retina Vitreous 2(1), 25 (2016).
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Invest. Ophthalmol. Vis. Sci. (3)

L. Zhang, K. Lee, M. Niemeijer, R. F. Mullins, M. Sonka, and M. D. Abràmoff, “Automated segmentation of the choroid from clinical SD-OCT,” Invest. Ophthalmol. Vis. Sci. 53(12), 7510–7519 (2012).
[Crossref] [PubMed]

R. F. Mullins, M. N. Johnson, E. A. Faidley, J. M. Skeie, and J. Huang, “Choriocapillaris vascular dropout related to density of drusen in human eyes with early age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52(3), 1606–1612 (2011).
[Crossref] [PubMed]

P. Jirarattanasopa, S. Ooto, I. Nakata, A. Tsujikawa, K. Yamashiro, A. Oishi, and N. Yoshimura, “Choroidal thickness, vascular hyperpermeability, and complement factor H in age-related macular degeneration and polypoidal choroidal vasculopathy,” Invest. Ophthalmol. Vis. Sci. 53(7), 3663–3672 (2012).
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H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P. A. Heng, “DCAN: Deep contour-aware networks for object instance segmentation from histology images,” Med. Image Anal. 36, 135–146 (2017).
[Crossref] [PubMed]

Neurocomputing (1)

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from OpticalCoherence Tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
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J. Dolz, C. Desrosiers, and I. Ben Ayed, “3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study,” Neuroimage 170, 456–470 (2018).
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Prog. Retin. Eye Res. (1)

D. L. Nickla and J. Wallman, “The multifunctional choroid,” Prog. Retin. Eye Res. 29(2), 144–168 (2010).
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J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and F. F. Li, “ImageNet: A large-scale hierarchical image database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009), 248–255.
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A. Vedaldi and K. Lenc, “MatConvNet: Convolutional Neural Networks for MATLAB,” in ACM International Conference on Multimedia, 2015), 689–692.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Comput. Sci. (2014).

G. M. Lin, Anton; Shen, Chunhua; Reid, Ian, “RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation,” eprint arXiv:1611.06612 (2016).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 770–778 (2015).

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the Devil in the Details: Delving Deep into Convolutional Nets,” Comput. Sci. (2014).

A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,” Adv. Neural Inf. Process. Syst. (2016).

N. Srinath, A. Patil, V. K. Kumar, S. Jana, J. Chhablani, and A. Richhariya, “Automated detection of choroid boundary and vessels in optical coherence tomography images,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), 166–169.
[Crossref]

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Figures (5)

Fig. 1
Fig. 1 Examples of raw SS-OCT images and their annotations by the two clinicians. The left column (A, D, G) shows the raw images; Middle column (B, E, H) is the annotations by clinician 1; and the right column (C, F, I) is the annotations by clinician 2.
Fig. 2
Fig. 2 Overview of the RefineNet for choroidal vessels segmentation.
Fig. 3
Fig. 3 Randomly selected segmentation results (rows) from three patient studies (in columns). (a) input images, (b, c, d, e) segmentation results derived from RefineNet, VGG-FCN, AT and LS; and (f, g) manual annotations from clinician 1 (C1) and clinician 2 (C2).
Fig. 4
Fig. 4 Bland–Altman plots comparing inter-observer variability. C1: clinician 1; C2: clinician 2; AT: adaptive thresholding; LS: level set.
Fig. 5
Fig. 5 Difference of high myopia and normal study. A: High Myopia; B: Normal (Emmetropia).

Tables (1)

Tables Icon

Table 1 Segmentation results and inter-observer variabilities among different methods.

Equations (4)

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

Y= U S ( F S ( I;θ );φ)
argmin θ,φ L(Y,Z|θ,φ).
SA= | AP |+| NP | | AP |+| NP |+| FP |
MAD=|P1P2|×R

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