Abstract

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm2 OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

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

Full Article  |  PDF Article
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  1. S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
    [Crossref] [PubMed]
  2. J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
    [Crossref] [PubMed]
  3. A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).
  4. T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
    [Crossref] [PubMed]
  5. M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
    [Crossref] [PubMed]
  6. T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
    [Crossref] [PubMed]
  7. T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
    [Crossref] [PubMed]
  8. 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] [PubMed]
  9. 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 (2015), pp. 234–241.
    [Crossref]
  10. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.
  11. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in AAAI (2017).
  12. 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.
  13. M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).
  14. T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” arXiv Prepr. arXiv1811.07738 (2018).
  15. D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
    [Crossref] [PubMed]
  16. G. Litjens, T. Kooi, B. E. 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]
  17. F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9(4), 1545–1569 (2018).
    [Crossref] [PubMed]
  18. C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
    [Crossref] [PubMed]
  19. M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
    [Crossref] [PubMed]
  20. C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration,” arXiv Prepr. arXiv1612.04891 (2016).
  21. D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
    [Crossref] [PubMed]
  22. 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]
  23. S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).
  24. J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
    [Crossref] [PubMed]
  25. 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]
  26. 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]
  27. C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).
  28. Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
    [Crossref] [PubMed]
  29. 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] [PubMed]
  30. D. P. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” AIP Conf. Proc. 1631, 58–62 (2014).
  31. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
    [Crossref]
  32. R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
    [Crossref] [PubMed]
  33. P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
    [Crossref] [PubMed]
  34. A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
    [Crossref] [PubMed]
  35. T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
    [Crossref] [PubMed]
  36. 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] [PubMed]
  37. 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] [PubMed]

2019 (2)

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref] [PubMed]

2018 (8)

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] [PubMed]

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

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] [PubMed]

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9(4), 1545–1569 (2018).
[Crossref] [PubMed]

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref] [PubMed]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

2017 (7)

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]

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]

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]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. 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]

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

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] [PubMed]

2016 (4)

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] [PubMed]

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

2015 (2)

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

2014 (1)

D. P. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” AIP Conf. Proc. 1631, 58–62 (2014).

2012 (1)

1984 (1)

T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
[Crossref] [PubMed]

Agemy, S. A.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Alibhai, A. Y.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Alom, M. Z.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

Alonso-Caneiro, D.

Arya, M.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Asari, V. K.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

Aung, T.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Ba, J.

D. P. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” AIP Conf. Proc. 1631, 58–62 (2014).

Bailey, S. T.

Bailey, S. T. T.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Baughman, D. M.

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration,” arXiv Prepr. arXiv1612.04891 (2016).

Baumal, C. R.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Baxter, S. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Bejnordi, B. E.

G. Litjens, T. Kooi, B. E. 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]

Bhavsar, K.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Brox, T.

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 (2015), pp. 234–241.
[Crossref]

Cai, W.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Camino, A.

Campbell, J. P.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

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] [PubMed]

Campbell, J. P. P.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Carrasco-Zevallos, O.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Chai, H.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Chen, S.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Chin, K. S.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Chui, T.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Ciompi, F.

G. Litjens, T. Kooi, B. E. 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]

Cole, E.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Collins, M. J.

Conjeti, S.

Cunefare, D.

Dang, S.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

de Freitas, A. Z.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

De Pretto, L. R.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Deruyter, N. P.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Devalla, S. K.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Dong, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Dongye, C.

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

Duan, Y.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Duker, J. S.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Eter, N.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

Fawzi, A. A.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Fischer, P.

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 (2015), pp. 234–241.
[Crossref]

Flaxel, C. J.

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Flaxel, C. J. J.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Fu, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Fujimoto, J. G.

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
[Crossref] [PubMed]

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Gao, S. S.

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Garcia, P. M.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Gentile, R. C.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Ghafoorian, M.

G. Litjens, T. Kooi, B. E. 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]

Girard, M. J. A.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Goldbaum, M.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Guo, Y.

Guymer, R. H.

Hagag, A. M.

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

Hamwood, J.

Hasan, M.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
[Crossref]

Hewett, S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Hornegger, J.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
[Crossref] [PubMed]

Hou, R.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Hoyng, C.

Hsiao, Y.-S.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Huang, D.

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
[Crossref] [PubMed]

Huang, G.

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.

Husvogt, L.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Huu, V. A. N.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Hwang, T.

Hwang, T. S.

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] [PubMed]

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Hwang, T. S. S.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Jalali, S.

T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” arXiv Prepr. arXiv1811.07738 (2018).

Jampol, L. M.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Jia, Y.

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
[Crossref] [PubMed]

Karri, S. P. K.

Katouzian, A.

Keane, P. A.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Kermany, D. S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Kingma, D. P. P.

D. P. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” AIP Conf. Proc. 1631, 58–62 (2014).

Ko, T.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Kooi, T.

G. Litjens, T. Kooi, B. E. 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]

Kraus, M. F.

Laibacher, T.

T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” arXiv Prepr. arXiv1811.07738 (2018).

Lauer, A. K.

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Lauer, A. K. K.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Lauermann, J. L.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref] [PubMed]

Lee, A. Y.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration,” arXiv Prepr. arXiv1612.04891 (2016).

Lee, B.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Lee, C. S.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration,” arXiv Prepr. arXiv1612.04891 (2016).

Lee, J. G.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Lewis, M. A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Li, C.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Li, O.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Li, S.

Liang, H.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Liefers, B.

Lin, P.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Litjens, G.

G. Litjens, T. Kooi, B. E. 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]

Liu, J. J.

Liu, L.

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref] [PubMed]

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Liu, Z.

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.

Lu, C. D.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Mari, J.-M.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

McGowan, M.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

McKeown, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Moult, E. M.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Muraoka, K.

T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
[Crossref] [PubMed]

Navab, N.

Nesper, P. L.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Niki, T.

T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
[Crossref] [PubMed]

Novais, E. A.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Onishi, A. C.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Or, C.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Pasquale, L. R.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Pei, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Peng, L.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Perera, S.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Ploner, S.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Potsaid, B.

Prasadha, M. K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Raman, R.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Read, S. A.

Reichel, E.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
[Crossref]

Renukanand, P. K.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Roberts, P. K.

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

Rokem, A.

Rokem, A. S.

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Ronneberger, O.

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 (2015), pp. 234–241.
[Crossref]

Rosen, R. B.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Roy, A. G.

Sadda, S. R.

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

Sánchez, C. I.

Schmetterer, L.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Schottenhamml, J.

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Schreur, V.

Scripsema, N. K.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Setio, A. A. A.

G. Litjens, T. Kooi, B. E. 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]

Shah, C. M.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Sheet, D.

Shi, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Shi, W.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Shimizu, K.

T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
[Crossref] [PubMed]

Singer, M. A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Smith, A.

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

Spaide, R. F.

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

Sreedhar, B. K.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Staurenghi, G.

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

Strouthidis, N. G.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Subhash, H.

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.

Sun, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Tafreshi, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Taha, T. M.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

Tan, G. S. W.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Tan, O.

Theelen, T.

Thiery, A. H.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Ting, D. S. W.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Ting, M. Y. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Tokayer, J.

Treder, M.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref] [PubMed]

Tufail, A.

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Tun, T. A.

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

Tyring, A. J.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Valentim, C. C. S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

van Asten, F.

van der Laak, J. A. W. M.

G. Litjens, T. Kooi, B. E. 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]

Van Der Maaten, L.

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.

van Ginneken, B.

van Grinsven, M. J. J. P.

Venhuizen, F. G.

Vincent, S. J.

Wachinger, C.

Waheed, N. K.

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Wang, C.

Wang, J.

Wang, R. K.

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Wang, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Wang, Y.

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.

Wen, C.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Weyde, T.

T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” arXiv Prepr. arXiv1811.07738 (2018).

Wilson, D. J.

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

Wilson, D. J. J.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Witkin, A. J.

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

Wong, T. Y.

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Wu, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Wu, Y.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Xia, H.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Xiao, S.

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Xu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Yakopcic, C.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

Yan, F.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Yang, G.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Yu, J.

Zhang, C. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Zhang, E. D.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Zhang, K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Zhang, M.

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] [PubMed]

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Zhang, Q.

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

Zhang, R.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Zhang, X.

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
[Crossref]

Zheng, L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Zhou, Q.

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

Zhu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Ziyar, I.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

AIP Conf. Proc. (1)

D. P. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” AIP Conf. Proc. 1631, 58–62 (2014).

Biomed. Opt. Express (11)

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] [PubMed]

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] [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]

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]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (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]

F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9(4), 1545–1569 (2018).
[Crossref] [PubMed]

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref] [PubMed]

Br. J. Ophthalmol. (1)

D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, and T. Y. Wong, “Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol. 103(2), 167–175 (2019).
[Crossref] [PubMed]

Cell (1)

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131 (2018).
[Crossref] [PubMed]

Graefes Arch. Clin. Exp. Ophthalmol. (1)

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefes Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref] [PubMed]

Invest. Ophthalmol. Vis. Sci. (2)

M. Zhang, T. S. Hwang, C. Dongye, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy,” Invest. Ophthalmol. Vis. Sci. 57(13), 5101–5106 (2016).
[Crossref] [PubMed]

P. L. Nesper, P. K. Roberts, A. C. Onishi, H. Chai, L. Liu, L. M. Jampol, and A. A. Fawzi, “Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO307 (2017).
[Crossref] [PubMed]

JAMA Ophthalmol. (2)

T. S. Hwang, A. M. Hagag, J. Wang, M. Zhang, A. Smith, D. J. Wilson, D. Huang, and Y. Jia, “Automated quantification of nonperfusion areas in 3 vascular plexuses with optical coherence tomography angiography in eyes of patients with diabetes,” JAMA Ophthalmol. 136(8), 929–936 (2018).
[Crossref] [PubMed]

T. S. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. P. Campbell, P. Lin, S. T. T. Bailey, C. J. J. Flaxel, A. K. K. Lauer, D. J. J. Wilson, D. Huang, and Y. Jia, “Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy,” JAMA Ophthalmol. 134(12), 1411–1419 (2016).
[Crossref] [PubMed]

Med. Image Anal. (1)

G. Litjens, T. Kooi, B. E. 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]

Ophthalmology (1)

T. Niki, K. Muraoka, and K. Shimizu, “Distribution of capillary nonperfusion in early-stage diabetic retinopathy,” Ophthalmology 91(12), 1431–1439 (1984).
[Crossref] [PubMed]

Opt. Express (1)

Prog. Retin. Eye Res. (1)

R. F. Spaide, J. G. Fujimoto, N. K. Waheed, S. R. Sadda, and G. Staurenghi, “Optical coherence tomography angiography,” Prog. Retin. Eye Res. 64, 1–55 (2018).
[Crossref] [PubMed]

Retina (3)

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref] [PubMed]

S. A. Agemy, N. K. Scripsema, C. M. Shah, T. Chui, P. M. Garcia, J. G. Lee, R. C. Gentile, Y.-S. Hsiao, Q. Zhou, T. Ko, and R. B. Rosen, “Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients,” Retina 35(11), 2353–2363 (2015).
[Crossref] [PubMed]

J. Schottenhamml, E. M. Moult, S. Ploner, B. Lee, E. A. Novais, E. Cole, S. Dang, C. D. Lu, L. Husvogt, N. K. Waheed, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography,” Retina 36(Suppl 1), S93–S101 (2016).
[Crossref] [PubMed]

Other (11)

A. Y. Alibhai, L. R. De Pretto, E. M. Moult, C. Or, M. Arya, M. McGowan, O. Carrasco-Zevallos, B. Lee, S. Chen, C. R. Baumal, A. J. Witkin, E. Reichel, A. Z. de Freitas, J. S. Duker, J. G. Fujimoto, and N. K. Waheed, “Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography,” Retina, epub ahead of print (2018).

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 (2015), pp. 234–241.
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 4, pp. 770–778.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in AAAI (2017).

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.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv Prepr. arXiv1802.06955 (2018).

T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” arXiv Prepr. arXiv1811.07738 (2018).

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration,” arXiv Prepr. arXiv1612.04891 (2016).

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “DRUNET: A dilated-residual U-Net deep learning network to digitally stain optic nervehead tissues in optical coherence tomography images,” arXiv Prepr. arXiv1803.00232 (2018).

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034.
[Crossref]

C. S. Lee, A. J. Tyring, Y. Wu, S. Xiao, A. S. Rokem, N. P. Deruyter, Q. Zhang, A. Tufail, R. K. Wang, and A. Y. Lee, “Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,” arXiv Prepr. arXiv1802.08925 (2018).

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Figures (8)

Fig. 1
Fig. 1 Data acquisition for MEDnet-V2. (A) Segmentation results of the retinal layer boundaries on a B-scan. (B) Definition of the superficial vascular complex (SVC) slab. (C) SVC angiogram produced by maximum projection of the OCTA data within the SVC slab. (D) Definition of the inner retina slab. (E) Reflectance image of the inner retina produced by mean projection of OCT data within the inner retina slab. (F) Thickness map of the inner retina. ILM — inner limiting membrane. NFL — nerve fiber layer. GCL — ganglion cell layer. IPL — inner plexiform layer. INL — inner nuclear layer. OPL — outer plexiform layer. ONL — outer nuclear layer. EZ — ellipsoid zone. RPE — retinal pigment epithelium. BM — Bruch’s membrane. SVC — Superficial vascular complex. DVC – Deep vascular complex. B – Boundary.
Fig. 2
Fig. 2 The brief network architecture of MEDnet-V2. (A) OCT reflectance image of the inner retina. (B) Gaussian-filtered reflectance intensity map of the inner retina. (C) Inner retinal thickness map. (D) The en face angiogram of the superficial vascular complex. (E1-E3) Three convolution networks with the same structure. (F) Detection result with probability maps for perfusion loss (blue) and signal reduction artifacts (yellow).
Fig. 3
Fig. 3 (A) Network architecture of subnetworks in MEDnet-V2. (B) Multi-scale convolutional block. (C-D) Residual blocks from ResNet.
Fig. 4
Fig. 4 Manual delineation of ground truth for training. (A) The in-house graphical user interface software. (B) Three experts delineated ground truth maps for nonperfusion area (green) and signal reduction artifacts (yellow) overlaid on the superficial vascular complex angiograms. (C) The final ground truth map overlaid on the superficial vascular complex angiogram.
Fig. 5
Fig. 5 Representative input data set. (A) En face angiogram of superficial vascular complex from a patient with diabetic retinopathy. (B) Inner retinal thickness map. (C) Reflectance image acquired by projecting the reflectance OCT data within the inner retina. (D) The ground truth map of the nonperfusion area (green) and signal reduction artifact (yellow) overlaid on the superficial vascular complex angiogram.
Fig. 6
Fig. 6 Results of simulated signal reduction artifacts on healthy controls by MEDnet-V2. (A1-D1) Reference scan under normal conditions. (A2-D2) Scan with simulated pupil vignetting. (A3-D3) Scan with simulated floater shadows. (A4-D4) Scan with 1 diopter defocus. (A5-D5) Scan with 2 diopters defocus. (A6-D6) Scan with 3 diopters defocus. First row (A), en face inner retinal reflectance images. Second row (B), en face angiograms of the superficial vascular complex. Third row (C), ground truth (green) of the nonperfusion areas, overlaid on the en face angiograms. Last row (D), the predicted results of nonperfusion areas (blue) and signal reduction artifacts (yellow) by MEDnet-V2 overlaid on the en face angiograms.
Fig. 7
Fig. 7 Results of nonperfusion area detection on clinical cases. (A1-D1) signal reduction artifacts connected to the macular area on a healthy control. (A2-D2) A healthy case with signal reduction artifacts caused by floater shadows and vignetting. (A3-D3) Mild to moderate DR case with signal reduction artifacts. (A4-D4) A severe diabetic retinopathy (DR) case with no signal reduction artifacts. (A5-D5) Severe DR case with strong signal reduction artifacts. First row (A), inner retinal reflectance en face images. Second row (B), en face superficial vascular complex angiograms. Third row (C), the ground truth (green) of the nonperfusion areas, overlaid on the en face angiograms. Last row (D), the predicted results of nonperfusion areas (blue) and signal reduction artifacts (yellow) by MEDnet-V2 overlaid on the en face angiograms.
Fig. 8
Fig. 8 The effect of defocus on nonperfusion area detection by MEDnet-V2.

Tables (3)

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Table 1 Data set used in MedNet-V2

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Table 2 Agreement (in pixels) between automated detection and manual delineation of nonperfusion area (mean ± standard deviation)

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Table 3 Intra-visit repeatability of MEDnet-V2 and manual delineation on NPA detection (SSI≥55)

Equations (5)

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M=φ( 1 N i=1 N G(h, σ i )(I I ¯ + 1 N ) ).
L= i=1 N J i × w i , i=1 N w i =1. J=( 1 x y( x )× y ^ ( x )+α x ( y( x )+ y ^ ( x ) ) x y( x )× y ^ ( x )+α )×α.
Accuracy= TP+TN TP+FP+TN+FN ,Sepcificity= TN TN+FP , Sensitivity= TP TP+FN ,Dice= 2×TP 2×TP+FP+FN .
P= 1 N i=1 N s i 2 .
C= P 1 N i=1 N μ i .

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