Abstract

Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (5)

2018 (11)

Y. Guo, A. Camino, J. Wang, D. Huang, T. S. Hwang, and Y. Jia, “Mednet, a neural network for automated detection of avascular area in oct angiography,” Biomed. Opt. Express 9(11), 5147–5158 (2018).
[Crossref]

U. Schmidt-Erfurth, A. Sadeghipour, B. S. Gerendas, S. M. Waldstein, and H. Bogunović, “Artificial intelligence in retina,” Prog. Retinal Eye Res. 67, 1–29 (2018).
[Crossref]

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,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref]

A. D. Treister, P. L. Nesper, A. E. Fayed, M. K. Gill, R. G. Mirza, and A. A. Fawzi, “Prevalence of subclinical cnv and choriocapillaris nonperfusion in fellow eyes of unilateral exudative amd on oct angiography,” Transl. Vis. Sci. & Technol. 7(5), 19 (2018).
[Crossref]

Y. Guo, A. Camino, M. Zhang, J. Wang, D. Huang, T. Hwang, and Y. Jia, “Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography,” Biomed. Opt. Express 9(9), 4429–4442 (2018).
[Crossref]

P. L. Nesper, B. T. Soetikno, A. D. Treister, and A. A. Fawzi, “Volume-rendered projection-resolved oct angiography: 3d lesion complexity is associated with therapy response in wet age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 59(5), 1944–1952 (2018).
[Crossref]

J. Xue, A. Camino, S. T. Bailey, X. Liu, D. Li, and Y. Jia, “Automatic quantification of choroidal neovascularization lesion area on oct angiography based on density cell-like p systems with active membranes,” Biomed. Opt. Express 9(7), 3208–3219 (2018).
[Crossref]

M. Al-Sheikh, N. A. Iafe, N. Phasukkijwatana, S. R. Sadda, and D. Sarraf, “Biomarkers of neovascular activity in age-related macular degeneration using optical coherence tomography angiography,” Retina 38(2), 220–230 (2018).
[Crossref]

R. C. Patel, J. Wang, T. S. Hwang, M. Zhang, S. S. Gao, M. E. Pennesi, S. T. Bailey, B. J. Lujan, X. Wang, D. J. Wilson, D. Huang, and Y. Jia, “Plexus-specific detection of retinal vascular pathologic conditions with projection-resolved oct angiography,” Ophthalmol. Retin. 2(8), 816–826 (2018).
[Crossref]

R. Patel, J. Wang, J. P. Campbell, L. Kiang, A. Lauer, C. Flaxel, T. Hwang, B. Lujan, D. Huang, S. T. Bailey, and Y. Jia, “Classification of choroidal neovascularization using projection-resolved optical coherence tomographic angiography,” Invest. Ophthalmol. Visual Sci. 59(10), 4285–4291 (2018).
[Crossref]

X. Wei, A. Camino, S. Pi, W. Cepurna, D. Huang, J. C. Morrison, and Y. Jia, “Fast and robust standard-deviation-based method for bulk motion compensation in phase-based functional oct,” Opt. Lett. 43(9), 2204–2207 (2018).
[Crossref]

2017 (7)

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography,” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref]

K. V. Bhavsar, Y. Jia, J. Wang, R. C. Patel, A. K. Lauer, D. Huang, and S. T. Bailey, “Projection-resolved optical coherence tomography angiography exhibiting early flow prior to clinically observed retinal angiomatous proliferation,” Am. J. Ophthalmol. Case Reports 8, 53–57 (2017).
[Crossref]

A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
[Crossref]

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

Q. Zhang, C.-L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. R. de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. Wang, “Automated quantitation of choroidal neovascularization: a comparison study between spectral-domain and swept-source oct angiograms,” Invest. Ophthalmol. Visual Sci. 58(3), 1506–1513 (2017).
[Crossref]

C. Zhu, B. Zou, R. Zhao, J. Cui, X. Duan, Z. Chen, and Y. Liang, “Retinal vessel segmentation in colour fundus images using extreme learning machine,” Comput. Med. Imaging Graph. 55, 68–77 (2017).
[Crossref]

J. Mo and L. Zhang, “Multi-level deep supervised networks for retinal vessel segmentation,” Int. journal computer assisted radiology surgery 12(12), 2181–2193 (2017).
[Crossref]

2016 (1)

2015 (8)

R. F. Spaide, J. G. Fujimoto, and N. K. Waheed, “Image artifacts in optical coherence angiography,” Retina 35(11), 2163–2180 (2015).
[Crossref]

L. Liu, S. S. Gao, S. T. Bailey, D. Huang, D. Li, and Y. Jia, “Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography,” Biomed. Opt. Express 6(9), 3564–3576 (2015).
[Crossref]

M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref]

A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in oct micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
[Crossref]

M. A. Bonini Filho, E. Talisa, D. Ferrara, M. Adhi, C. R. Baumal, A. J. Witkin, E. Reichel, J. S. Duker, and N. K. Waheed, “Association of choroidal neovascularization and central serous chorioretinopathy with optical coherence tomography angiography,” JAMA Ophthalmol. 133(8), 899–906 (2015).
[Crossref]

L. Kuehlewein, M. Bansal, T. L. Lenis, N. A. Iafe, S. R. Sadda, M. A. Bonini Filho, E. Talisa, N. K. Waheed, J. S. Duker, and D. Sarraf, “Optical coherence tomography angiography of type 1 neovascularization in age-related macular degeneration,” Am. J. Ophthalmol. 160(4), 739–748.e2 (2015).
[Crossref]

M. Inoue, C. Balaratnasingam, and K. B. Freund, “Optical coherence tomography angiography of polypoidal choroidal vasculopathy and polypoidal choroidal neovascularization,” Retina 35(11), 2265–2274 (2015).
[Crossref]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. 112(18), E2395–E2402 (2015).
[Crossref]

2014 (1)

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref]

2012 (1)

2008 (1)

R. D. Jager, W. F. Mieler, and J. W. Miller, “Age-related macular degeneration,” N. Engl. J. Med. 358(24), 2606–2617 (2008).
[Crossref]

2007 (1)

R. Perfetti, E. Ricci, D. Casali, and G. Costantini, “Cellular neural networks with virtual template expansion for retinal vessel segmentation,” IEEE Trans. Circuits Syst. II 54(2), 141–145 (2007).
[Crossref]

2006 (2)

P. T. De Jong, “Age-related macular degeneration,” N. Engl. J. Med. 355(14), 1474–1485 (2006).
[Crossref]

L. A. Donoso, D. Kim, A. Frost, A. Callahan, and G. Hageman, “The role of inflammation in the pathogenesis of age-related macular degeneration,” Surv. Ophthalmol. 51(2), 137–152 (2006).
[Crossref]

2004 (3)

H. E. Grossniklaus and W. R. Green, “Choroidal neovascularization,” Am. J. Ophthalmol. 137(3), 496–503 (2004).
[Crossref]

The Eye Disease Prevalence Research Group, “Causes and prevalence of visual impairment among adults in the united states,” Arch. Ophthalmol. 122(4), 477–485 (2004).
[Crossref]

The Eye Disease Prevalence Research Group, Prevalence of age-related macular degeneration in the united states,” Arch. Ophthalmol. 122(4), 564–572 (2004).
[Crossref]

2003 (1)

P. E. Stanga, J. I. Lim, and P. Hamilton, “Indocyanine green angiography in chorioretinal diseases: indications and interpretation: an evidence-based update,” Ophthalmology 110(1), 15–21 (2003).
[Crossref]

1998 (1)

M. Lopez-Saez, E. Ordoqui, P. Tornero, A. Baeza, T. Sainza, e. J. Zubeldia, M. Baeza, and M. L. Baeza, “Fluorescein-induced allergic reaction,” Ann. Allergy, Asthma, Immunol. 81(5), 428–430 (1998).
[Crossref]

1996 (1)

M. R. Hee, C. R. Baumal, C. A. Puliafito, J. S. Duker, E. Reichel, J. R. Wilkins, J. G. Coker, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Optical coherence tomography of age-related macular degeneration and choroidal neovascularization,” Ophthalmology 103(8), 1260–1270 (1996).
[Crossref]

Adam, H.

L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587 (2017).

Adhi, M.

M. A. Bonini Filho, E. Talisa, D. Ferrara, M. Adhi, C. R. Baumal, A. J. Witkin, E. Reichel, J. S. Duker, and N. K. Waheed, “Association of choroidal neovascularization and central serous chorioretinopathy with optical coherence tomography angiography,” JAMA Ophthalmol. 133(8), 899–906 (2015).
[Crossref]

Al-Sheikh, M.

M. Al-Sheikh, N. A. Iafe, N. Phasukkijwatana, S. R. Sadda, and D. Sarraf, “Biomarkers of neovascular activity in age-related macular degeneration using optical coherence tomography angiography,” Retina 38(2), 220–230 (2018).
[Crossref]

An, L.

Q. Zhang, C.-L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. R. de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. Wang, “Automated quantitation of choroidal neovascularization: a comparison study between spectral-domain and swept-source oct angiograms,” Invest. Ophthalmol. Visual Sci. 58(3), 1506–1513 (2017).
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Antony, B.

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M. Al-Sheikh, N. A. Iafe, N. Phasukkijwatana, S. R. Sadda, and D. Sarraf, “Biomarkers of neovascular activity in age-related macular degeneration using optical coherence tomography angiography,” Retina 38(2), 220–230 (2018).
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L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587 (2017).

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S. Maetschke, B. Antony, H. Ishikawa, G. Wollstein, J. Schuman, and R. Garnavi, “A feature agnostic approach for glaucoma detection in oct volumes,” PLoS One 14(7), e0219126 (2019).
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M. R. Hee, C. R. Baumal, C. A. Puliafito, J. S. Duker, E. Reichel, J. R. Wilkins, J. G. Coker, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Optical coherence tomography of age-related macular degeneration and choroidal neovascularization,” Ophthalmology 103(8), 1260–1270 (1996).
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P. L. Nesper, B. T. Soetikno, A. D. Treister, and A. A. Fawzi, “Volume-rendered projection-resolved oct angiography: 3d lesion complexity is associated with therapy response in wet age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 59(5), 1944–1952 (2018).
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Swanson, E. A.

M. R. Hee, C. R. Baumal, C. A. Puliafito, J. S. Duker, E. Reichel, J. R. Wilkins, J. G. Coker, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Optical coherence tomography of age-related macular degeneration and choroidal neovascularization,” Ophthalmology 103(8), 1260–1270 (1996).
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P. L. Nesper, B. T. Soetikno, A. D. Treister, and A. A. Fawzi, “Volume-rendered projection-resolved oct angiography: 3d lesion complexity is associated with therapy response in wet age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 59(5), 1944–1952 (2018).
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R. F. Spaide, J. G. Fujimoto, and N. K. Waheed, “Image artifacts in optical coherence angiography,” Retina 35(11), 2163–2180 (2015).
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U. Schmidt-Erfurth, A. Sadeghipour, B. S. Gerendas, S. M. Waldstein, and H. Bogunović, “Artificial intelligence in retina,” Prog. Retinal Eye Res. 67, 1–29 (2018).
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Y. Guo, A. Camino, M. Zhang, J. Wang, D. Huang, T. Hwang, and Y. Jia, “Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography,” Biomed. Opt. Express 9(9), 4429–4442 (2018).
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Y. Guo, A. Camino, J. Wang, D. Huang, T. S. Hwang, and Y. Jia, “Mednet, a neural network for automated detection of avascular area in oct angiography,” Biomed. Opt. Express 9(11), 5147–5158 (2018).
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A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
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J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography,” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref]

K. V. Bhavsar, Y. Jia, J. Wang, R. C. Patel, A. K. Lauer, D. Huang, and S. T. Bailey, “Projection-resolved optical coherence tomography angiography exhibiting early flow prior to clinically observed retinal angiomatous proliferation,” Am. J. Ophthalmol. Case Reports 8, 53–57 (2017).
[Crossref]

M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
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Wang, R. K.

Q. Zhang, C.-L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. R. de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. Wang, “Automated quantitation of choroidal neovascularization: a comparison study between spectral-domain and swept-source oct angiograms,” Invest. Ophthalmol. Visual Sci. 58(3), 1506–1513 (2017).
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A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in oct micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
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Wang, Y.

Wei, X.

Weinberger, K. Q.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), pp. 4700–4708.

Wilkins, J. R.

M. R. Hee, C. R. Baumal, C. A. Puliafito, J. S. Duker, E. Reichel, J. R. Wilkins, J. G. Coker, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Optical coherence tomography of age-related macular degeneration and choroidal neovascularization,” Ophthalmology 103(8), 1260–1270 (1996).
[Crossref]

Wilson, D. J.

R. C. Patel, J. Wang, T. S. Hwang, M. Zhang, S. S. Gao, M. E. Pennesi, S. T. Bailey, B. J. Lujan, X. Wang, D. J. Wilson, D. Huang, and Y. Jia, “Plexus-specific detection of retinal vascular pathologic conditions with projection-resolved oct angiography,” Ophthalmol. Retin. 2(8), 816–826 (2018).
[Crossref]

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography,” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref]

M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
[Crossref]

M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. 112(18), E2395–E2402 (2015).
[Crossref]

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref]

Witkin, A. J.

M. A. Bonini Filho, E. Talisa, D. Ferrara, M. Adhi, C. R. Baumal, A. J. Witkin, E. Reichel, J. S. Duker, and N. K. Waheed, “Association of choroidal neovascularization and central serous chorioretinopathy with optical coherence tomography angiography,” JAMA Ophthalmol. 133(8), 899–906 (2015).
[Crossref]

Wollstein, G.

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Figures (14)

Fig. 1.
Fig. 1. CNV segmentation on challenging scans using a saliency-based algorithm. Small residual projection artifacts are excluded in the saliency map (A1&B1, highlighted by white arrows). Strong residual artifacts in CNV and non-CNV scans were over-segmented in the saliency map, providing false positives (A2&B2, A4&B4, highlighted by red arrows), while large CNV was under-segmented in the saliency map, producing false negatives (A3&B3, highlighted by green arrows).
Fig. 2.
Fig. 2. Comparison of non-CNV, including a healthy (column 1), diabetic retinopathy (DR, column 2), and dry AMD (column 3), and CNV (wet AMD, column 4) scans with en face outer retinal angiograms (row A) and cross-sectional structural OCT overlaid with OCTA (row B) showing inner retinal (violet), choroidal (red), and pathological outer retinal flow (yellow). Slab segmentation lines are the inner limiting membrane (violet), outer border of the outer plexiform layer (yellow), and Bruch’s membrane (green). White dotted lines in row A indicate the locations of the cross sections in row B. Red arrows indicate the pathologies in outer retina.
Fig. 3.
Fig. 3. Input angiographic image set. (A) Original (A1) and projection-resolved (PR) OCTA (A2) with inner retinal (violet), choroidal (red), and outer retinal (yellow) flow overlaid on structural OCT; (B) inner retinal angiogram, with white dotted line indicating the position of the B-scans in (A); (C) outer retinal angiogram generated from the original OCTA demonstrated in (A1); (D) outer retinal angiogram processed by slab -subtraction; (E) PR outer retinal angiogram. In (E) the entire CNV is preserved but some residual projection artifacts persist.
Fig. 4.
Fig. 4. Generation of outer retinal structural volume input. (A) Original structural OCT volume; (B) extracted outer retinal volume; (C) original cross-sectional OCT, with anatomic slab segmentation overlaid in violet (inner limiting membrane, ILM), yellow (outer plexiform layer, OPL), and green (Bruch’s membrane, BM); (D) segmented outer retinal cross section, resampled so that the volume has a constant voxel depth.
Fig. 5.
Fig. 5. Outline of the proposed automated CNV identification and segmentation method. Input consists of the original inner retinal angiogram and original, slab-subtracted, and projection-resolved (PR) outer retinal angiograms, and volumetric structural data from the outer retina. We trained two separate CNNs in order to segment the CNV membrane and vessels, respectively. The first one (CNN-M) segments CNV membrane and outputs a mask corresponding to its location (if it is present). The second (CNN-V) segments CNV vascular pixels within the CNV membrane output by the first CNN.
Fig. 6.
Fig. 6. CNN architecture for CNV membrane segmentation (CNN-M). The atrous kernel sizes ($Rate = 1\;to\;32$) are annotated below each encoder block. The number of kernels is annotated below each convolutional layer. Label $I$ and $I/2$ indicate operations on the full ($304\times 304-pixel$) and half-sized ($152\times 152-pixel$) image, respectively.
Fig. 7.
Fig. 7. Encoder block architecture. The number of kernels are annotated below each convolutional layer. Dots at intersections along the lines indicate connections between layers.
Fig. 8.
Fig. 8. CNN architecture for CNV vessel segmentation (CNN-V). The atrous kernel sizes ($Rate = 1\;to\;8$) are annotated below each encoder block. The number of kernels is annotated below the convolutional layer. Label beside the block is the image size; in this case, the network operates on the fully-sized($304\times 304-pixel$) image.
Fig. 9.
Fig. 9. Ground truth generation. (A) outer retina angiogram generated from projection resolved (PR)-OCTA; (B) CNV membrane outline drawn by an expert grader; (C) CNV vessel mask verified by an expert grader.
Fig. 10.
Fig. 10. CNV segmentation on scans with good image quality. (A) Projection-resolved (PR) outer retinal angiogram; (B) manually delineated CNV membrane (red outline) and vessel (white pixels) ground truths; (C) probability map output by the membrane segmentation CNN (CNN-M); (D) probability map output by the vessel segmentation CNN (CNN-V); (E) segmented CNV membrane (white outline, with $probability>0.5$) and vessels (with pixels of $probability>0.5$).
Fig. 11.
Fig. 11. CNV segmentation on challenging scans containing a wide range of flow rates. (A) Projection-resolved (PR) outer retinal angiogram; (B) manually delineated CNV membrane (red outline) and vessels (white pixels) ground truths; (C) probability map output by the membrane segmentation CNN (CNN-M); (D) probability map output by the vessel segmentation CNN (CNN-V); (E) segmented CNV membrane (white outline, with $probability>0.5$) and vessels (with pixels of $probability>0.5$). Large inter capillary space, highlighted by stars, were correctly included in the membrane area by the proposed algorithm.
Fig. 12.
Fig. 12. CNV segmentation on two cases with strong residual projection artifacts. Bottom row shows a special case with a retinal angiomatous proliferation lesion. (A) Projection-resolved (PR) outer retinal angiogram; (B) manually delineated CNV membrane (red outline) and vessel (white pixels) ground truths; (C) probability map output by the membrane segmentation CNN (CNN-M); (D) probability map output by the vessel segmentation CNN (CNN-V); (E) segmented CNV membrane (white outline, with $probability>0.5$) and vessels (with pixels of $probability>0.5$).
Fig. 13.
Fig. 13. CNV segmentation on scans with defocusing. (A) Projection-resolved (PR) outer retinal angiogram; (B) manually delineated ground truth of CNV membrane (red outline) and vessels (white pixels); (C) probability map output by the membrane segmentation CNN (CNN-M); (D) probability map output by the vessel segmentation CNN (CNN-V); (E) segmented CNV membrane (white outline, with $probability>0.5$) and vessels (with pixels of $probability>0.5$).
Fig. 14.
Fig. 14. The proposed method correctly classifies scans with no CNV present. Shown are a case with dry age-related macular degeneration (AMD; row 1) and diabetic retinopathy (DR; row 2). No CNV is delineated in ground truths (column B). Despite strong motion artifacts (column A), the proposed method’s probability map (column C&D) does not indicate any CNV, and so the algorithm correctly does not segment any membrane or vessels in the output (column E).

Tables (4)

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Table 1. Dataset for training and testing

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Table 2. CNV diagnostic accuracy

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Table 3. Agreement between CNV membrane outputs and ground truth ($mean \pm std$)

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Table 4. Comparison of repeatability among ground truth, saliency-based method and our proposed method

Equations (4)

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I O U = G T O u t G T O u t
p r e c i s i o n = T P T P + F P
r e c a l l = T P T P + F N
F 1 = 2 × p r e c i s i o n × r e c a l l p r e c i s i o n + r e c a l l