Abstract

Screening and assessing diabetic retinopathy (DR) are essential for reducing morbidity associated with diabetes. Macular ischemia is known to correlate with the severity of retinopathy. Recent studies have shown that optical coherence tomography angiography (OCTA), with intrinsic contrast from blood flow motion, is well suited for quantified analysis of the avascular area, which is potentially a useful biomarker in DR. In this study, we propose the first deep learning solution to segment the avascular area in OCTA of DR. The network design consists of a multi-scaled encoder-decoder neural network (MEDnet) to detect the non-perfusion area in 6 × 6 mm2 and in ultra-wide field retinal angiograms. Avascular areas were effectively detected in DR subjects of various disease stages as well as in the foveal avascular zone of healthy subjects.

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

Full Article  |  PDF Article
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2018 (5)

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]

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref] [PubMed]

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

2017 (6)

S. P. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (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]

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

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [PubMed]

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

2016 (7)

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (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, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[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]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref] [PubMed]

2015 (4)

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

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 3431–3440 (2015)

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

2014 (1)

2012 (3)

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]

D. A. Antonetti, R. Klein, and T. W. Gardner, “Diabetic Retinopathy,” N. Engl. J. Med. 366(13), 1227–1239 (2012).
[Crossref] [PubMed]

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

2004 (1)

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Aaker, G. D.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

Adamis, A. P.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Antonetti, D. A.

D. A. Antonetti, R. Klein, and T. W. Gardner, “Diabetic Retinopathy,” N. Engl. J. Med. 366(13), 1227–1239 (2012).
[Crossref] [PubMed]

Bailey, S. T.

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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, 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, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref] [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, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
[Crossref] [PubMed]

Beg, M. F.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Bhavsar, K.

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

Camino, A.

Campbell, J. P.

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. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

Chakraborty, D.

Chatterjee, J.

Chen, C.-L.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Chen, L.-C.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Chu, Z.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Comer, G. M.

Conjeti, S.

Cousins, S. W.

Cunefare, D.

D’Amico, D. J.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 3431–3440 (2015)

de Sisternes, L.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (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]

Durbin, M.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Ehrlich, J. R.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

Esser, C.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Flaxel, C. J.

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

Fujimoto, J. G.

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

Gao, S. S.

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref] [PubMed]

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

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

Gardner, T. W.

D. A. Antonetti, R. Klein, and T. W. Gardner, “Diabetic Retinopathy,” N. Engl. J. Med. 366(13), 1227–1239 (2012).
[Crossref] [PubMed]

Gregori, G.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Guymer, R. H.

Hagag, A. M.

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (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]

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), pp. 770–778.

Heisler, M.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Hornegger, J.

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

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (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]

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [PubMed]

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

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[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, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref] [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, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (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]

Hussain, A.

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

Hussain, N.

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

Hwang, T. S.

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]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (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]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref] [PubMed]

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (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]

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

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

Izatt, J. A.

Janicki, H.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Jia, Y.

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]

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref] [PubMed]

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [PubMed]

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

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (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, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[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, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref] [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, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (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]

Joussen, A. M.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Karri, S. P.

Karri, S. P. K.

Katouzian, A.

Kern, T. S.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Kim, L. A.

Kirchhof, B.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Kiss, S.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

Klein, R.

D. A. Antonetti, R. Klein, and T. W. Gardner, “Diabetic Retinopathy,” N. Engl. J. Med. 366(13), 1227–1239 (2012).
[Crossref] [PubMed]

Kociok, N.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Koizumi, K.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Kokkinos, I.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Kraus, M. F.

Lauer, A. K.

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

Le, M. L.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Lee, S.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Li, C.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Li, D.

Li, S.

Li, Y.

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

Lin, P.

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

Liu, G.

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

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

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

Liu, J. J.

Liu, L.

Loncaric, S.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 3431–3440 (2015)

Mammo, Z.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

McClintic, S. M.

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

Merkur, A.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Mettu, P. S.

Morrison, J. C.

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

Motulsky, E. H.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Murphy, K.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Nair, N.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

Navab, N.

Navajas, E.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Papandreou, G.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Pechauer, A. D.

Pennesi, M. E.

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref] [PubMed]

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

Potsaid, B.

Poulaki, V.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Prentašic, P.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

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), pp. 770–778.

Rosenfeld, P. J.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Roy, A. G.

Šarunic, M.

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 75008 (2016).
[Crossref] [PubMed]

Schraermeyer, U.

A. M. Joussen, V. Poulaki, M. L. Le, K. Koizumi, C. Esser, H. Janicki, U. Schraermeyer, N. Kociok, S. Fauser, B. Kirchhof, T. S. Kern, and A. P. Adamis, “A central role for inflammation in the pathogenesis of diabetic retinopathy,” FASEB J. 18(12), 1450–1452 (2004).
[Crossref] [PubMed]

Sharma, U.

Sheet, D.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 3431–3440 (2015)

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]

Srinivasan, P. P.

Su, J. P.

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

Subhash, H.

Sun, J.

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), pp. 770–778.

Takusagawa, H. L.

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

Tan, O.

Tokayer, J.

Wachinger, C.

Wang, C.

Wang, J.

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (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]

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [PubMed]

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

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

Wang, R. K.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Wang, Y.

Wang, Z.

Weleber, R. G.

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref] [PubMed]

Wessel, M. M.

M. M. Wessel, N. Nair, G. D. Aaker, J. R. Ehrlich, D. J. D’Amico, and S. Kiss, “Peripheral retinal ischaemia, as evaluated by ultra-widefield fluorescein angiography, is associated with diabetic macular oedema,” Br. J. Ophthalmol. 96(5), 694–698 (2012).
[Crossref] [PubMed]

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]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [PubMed]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[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]

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

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

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

Yang, J.

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

Yang, P.

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref] [PubMed]

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref] [PubMed]

Yuille, A. L.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref] [PubMed]

Zang, P.

G. Liu, J. Yang, J. Wang, Y. Li, P. Zang, Y. Jia, and D. Huang, “Extended axial imaging range, widefield swept source optical coherence tomography angiography,” J. Biophotonics 10(11), 1464–1472 (2017).
[Crossref] [PubMed]

Zhang, 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]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (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]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref] [PubMed]

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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]

N. Hussain, A. Hussain, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Diametric measurement of foveal avascular zone in healthy young adults using Optical Coherence Tomography Angiography,” Int. J. Retina Vitreous 2(OCT), 27–36 (2016).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

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

Zhang, Q.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Zhang, X.

T. S. Hwang, M. Zhang, K. Bhavsar, X. Zhang, J. P. Campbell, P. Lin, S. T. Bailey, C. J. Flaxel, A. K. Lauer, D. 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), pp. 770–778.

Zheng, F.

Q. Zhang, F. Zheng, E. H. Motulsky, G. Gregori, Z. Chu, C.-L. Chen, C. Li, L. de Sisternes, M. Durbin, P. J. Rosenfeld, and R. K. Wang, “A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography,” Invest. Ophthalmol. Vis. Sci. 59(1), 203–211 (2018).
[Crossref] [PubMed]

Biomed. Opt. Express (10)

P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
[Crossref] [PubMed]

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

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Figures (6)

Fig. 1
Fig. 1 Image processing for the generation of en face visualization of the OCT tissue reflectance and OCT angiography of the superficial vascular complex (SVC) slab. (A) Results of the layer segmentation on a B-scan, delineating seven retinal interfaces. (B) Definition of the SVC boundaries. (C-D) The mean projection of the OCT data within the SVC produce an en face visualization of the retinal reflectivity. (E-F) The maximum projection of the OCTA data within the SVC slab produces an en face image of the superficial retinal flow in the macular region. SVC – Superficial vascular complex. DVC – Deep vascular complex. B – Boundary.
Fig. 2
Fig. 2 (A) Network architecture of multi-scaled encoder-decoder neural network (MEDnet). (B) Kernel sizes of atrous convolution blocks with different dilation rates.
Fig. 3
Fig. 3 Representative data used for training. (A) The en face angiogram of the superficial vascular complex from a patient with diabetic retinopathy. (B) The reflectance image acquired by projecting the reflectance OCT data within the same slab used in (A). (C) The ground truth map of the avascular area. (D) Manually segmented avascular area, overlaid on the superficial vascular complex angiogram.
Fig. 4
Fig. 4 Results of the avascular area detection. (A1-D1) En face superficial vascular complex (SVC) angiograms of healthy and non-proliferative diabetic retinopathy (NPDR) subjects. (A2-D2) Probability maps of the avascular areas generated by MEDnet overlaid on the en face angiograms. (A3-D3) Region detected as avascular area overlaid on the en face angiograms. (A4-D4) Ground truth of the avascular areas generated manually by an expert grader, overlaid on the en face angiograms.
Fig. 5
Fig. 5 (A) Ultra-wide field OCTA of an eye with diabetic retinopathy obtained by montaging three 8 × 10mm2 wide field OCTA en face angiograms of the superficial vascular complex (SVC). (B) Avascular area detected on the eye represented in (A) overlaid on the en face angiogram of the SVC.
Fig. 6
Fig. 6 Results of the avascular area detection. (A1-C1) reflectance images generated by the mean projection of the reflectance OCT data within the superficial vascular complex (SVC) of (A) one diabetic subject without diabetic retinopathy (DR) and (B-C) two subjects with severe DR. (A2-C2) SVC angiograms. (A3-C3) Probability maps of the avascular areas generated by MEDnet overlaid on the corresponding en face angiograms. (A4-D4) Region detected as avascular area overlaid on the corresponding en face angiograms. The positions of arrows in A1-B4 are shadow area caused by vignetting or vitreous floaters. The positions of arrows in C1-C4 are motion artifacts that cause detection errors.

Tables (4)

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Table 1 Parameters of MEDnet’s layers

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Table 2 Data distribution of data set for MEDnet training (the number of subjects)

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Table 3 The hyper-parameters of MEDnet

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Table 4 Agreement (in pixels) between MEDnet and Ground Truth of the Avascular Area (mean ± standard deviation)

Equations (5)

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E=  1 N i=1 N ( y i y ^ i ) 2 
R=  i=1 p w i 2 
T= E+R  
l t =l* a t s
Accuracy= TP+TN TP+FP+TN+FN Precision=  TP TP+FP               Recall= TP TP+FN                     F1=2× Precision×Recall Precision+Recall

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