Abstract

Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading.

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

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2018 (1)

2017 (12)

K. M. Litts, R. F. Cooper, J. L. Duncan, and J. Carroll, “Photoreceptor-based biomarkers in AOSLO retinal imaging,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO255 (2017).
[Crossref] [PubMed]

M. Laslandes, M. Salas, C. K. Hitzenberger, and M. Pircher, “Increasing the field of view of adaptive optics scanning laser ophthalmoscopy,” Biomed. Opt. Express 8(11), 4811–4826 (2017).
[Crossref] [PubMed]

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. 114(3), 586–591 (2017).
[Crossref] [PubMed]

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

J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated photoreceptor cell identification on nonconfocal adaptive optics images using multiscale circular voting,” Invest. Ophthalmol. Vis. Sci. 58(11), 4477–4489 (2017).
[Crossref] [PubMed]

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
[Crossref] [PubMed]

S. P. K. 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]

X. Fei, J. Zhao, H. Zhao, D. Yun, and Y. Zhang, “Deblurring adaptive optics retinal images using deep convolutional neural networks,” Biomed. Opt. Express 8(12), 5675–5687 (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).
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M. Pircher and R. J. Zawadzki, “Review of adaptive optics OCT (AO-OCT): principles and applications for retinal imaging [Invited],” Biomed. Opt. Express 8(5), 2536–2562 (2017).
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Z. Liu, K. Kurokawa, F. Zhang, J. J. Lee, and D. T. Miller, “Imaging and quantifying ganglion cells and other transparent neurons in the living human retina,” Proc. Natl. Acad. Sci. U.S.A. 114(48), 12803–12808 (2017).
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2016 (9)

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
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M. Chen, R. F. Cooper, G. K. Han, J. Gee, D. H. Brainard, and J. I. W. Morgan, “Multi-modal automatic montaging of adaptive optics retinal images,” Biomed. Opt. Express 7(12), 4899–4918 (2016).
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P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
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Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
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M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

J. Lammer, S. G. Prager, M. C. Cheney, A. Ahmed, S. H. Radwan, S. A. Burns, P. S. Silva, and J. K. Sun, “Cone photoreceptor irregularity on adaptive optics scanning laser ophthalmoscopy correlates with severity of diabetic retinopathy and macular edemacone mosaic irregularity in diabetic eyes on AOSLO,” Invest. Ophthalmol. Vis. Sci. 57(15), 6624–6632 (2016).
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2015 (6)

A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu Rev Vis Sci 1(1), 19–50 (2015).
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L. Mariotti and N. Devaney, “Performance analysis of cone detection algorithms,” J. Opt. Soc. Am. A 32(4), 497–506 (2015).
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D. M. Bukowska, A. L. Chew, E. Huynh, I. Kashani, S. L. Wan, P. M. Wan, and F. K. Chen, “Semi-automated identification of cones in the human retina using circle Hough transform,” Biomed. Opt. Express 6(12), 4676–4693 (2015).
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J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61(10), 85–117 (2015).
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S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
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N. D. Shemonski, F. A. South, Y.-Z. Liu, S. G. Adie, P. S. Carney, and S. A. Boppart, “Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9(7), 440–443 (2015).
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2014 (3)

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref] [PubMed]

M. Lombardo, S. Serrao, and G. Lombardo, “Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images,” PLoS One 9(9), e107402 (2014).
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2013 (3)

2012 (5)

2011 (6)

A. Dubra and Y. Sulai, “Reflective afocal broadband adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(6), 1757–1768 (2011).
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A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref] [PubMed]

O. P. Kocaoglu, S. Lee, R. S. Jonnal, Q. Wang, A. E. Herde, J. C. Derby, W. Gao, and D. T. Miller, “Imaging cone photoreceptors in three dimensions and in time using ultrahigh resolution optical coherence tomography with adaptive optics,” Biomed. Opt. Express 2(4), 748–763 (2011).
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D. Merino, J. L. Duncan, P. Tiruveedhula, and A. Roorda, “Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(8), 2189–2201 (2011).
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Y. Kitaguchi, S. Kusaka, T. Yamaguchi, T. Mihashi, and T. Fujikado, “Detection of photoreceptor disruption by adaptive optics fundus imaging and fourier-domain optical coherence tomography in eyes with occult macular dystrophy,” Clin. Ophthalmol. 5, 345–351 (2011).
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M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

2010 (3)

2009 (1)

2008 (3)

2007 (4)

J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. H. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 48(7), 3283–3291 (2007).
[Crossref] [PubMed]

K. Y. Li and A. Roorda, “Automated identification of cone photoreceptors in adaptive optics retinal images,” J. Opt. Soc. Am. A 24(5), 1358–1363 (2007).
[Crossref] [PubMed]

B. Xue, S. S. Choi, N. Doble, and J. S. Werner, “Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera,” J. Opt. Soc. Am. A 24(5), 1364–1372 (2007).
[Crossref] [PubMed]

Y. Kitaguchi, K. Bessho, T. Yamaguchi, N. Nakazawa, T. Mihashi, and T. Fujikado, “In vivo measurements of cone photoreceptor spacing in myopic eyes from images obtained by an adaptive optics fundus camera,” Jpn. J. Ophthalmol. 51(6), 456–461 (2007).
[Crossref] [PubMed]

2006 (2)

D. Merino, C. Dainty, A. Bradu, and A. G. Podoleanu, “Adaptive optics enhanced simultaneous en-face optical coherence tomography and scanning laser ophthalmoscopy,” Opt. Express 14(8), 3345–3353 (2006).
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S. S. Choi, N. Doble, J. L. Hardy, S. M. Jones, J. L. Keltner, S. S. Olivier, and J. S. Werner, “In vivo imaging of the photoreceptor mosaic in retinal dystrophies and correlations with visual function,” Invest. Ophthalmol. Vis. Sci. 47(5), 2080–2092 (2006).
[Crossref] [PubMed]

2005 (1)

2004 (1)

J. Carroll, M. Neitz, H. Hofer, J. Neitz, and D. R. Williams, “Functional photoreceptor loss revealed with adaptive optics: An alternate cause of color blindness,” Proc. Natl. Acad. Sci. U.S.A. 101(22), 8461–8466 (2004).
[Crossref] [PubMed]

2002 (2)

1999 (1)

A. Roorda and D. R. Williams, “The arrangement of the three cone classes in the living human eye,” Nature 397(6719), 520–522 (1999).
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1948 (1)

T. Sørensen, “A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons,” Biol. Skr. 5(1), 1–34 (1948).

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
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Aboshiha, J.

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

Abràmoff, M. D.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Adie, S. G.

N. D. Shemonski, F. A. South, Y.-Z. Liu, S. G. Adie, P. S. Carney, and S. A. Boppart, “Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9(7), 440–443 (2015).
[Crossref] [PubMed]

Ahmed, A.

J. Lammer, S. G. Prager, M. C. Cheney, A. Ahmed, S. H. Radwan, S. A. Burns, P. S. Silva, and J. K. Sun, “Cone photoreceptor irregularity on adaptive optics scanning laser ophthalmoscopy correlates with severity of diabetic retinopathy and macular edemacone mosaic irregularity in diabetic eyes on AOSLO,” Invest. Ophthalmol. Vis. Sci. 57(15), 6624–6632 (2016).
[Crossref] [PubMed]

Ahnelt, P. K.

Amelon, R.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
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Ansari, R.

F. Mohammad, R. Ansari, J. Wanek, and M. Shahidi, “Frequency-based local content adaptive filtering algorithm for automated photoreceptor cell density quantification,” in Proceedings of IEEE International Conference on Image Processing, (IEEE, 2012), 2325–2328.
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Beck, S. C.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Beis, J.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
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Benedicenti, F.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Bergeles, C.

Bessho, K.

Y. Kitaguchi, K. Bessho, T. Yamaguchi, N. Nakazawa, T. Mihashi, and T. Fujikado, “In vivo measurements of cone photoreceptor spacing in myopic eyes from images obtained by an adaptive optics fundus camera,” Jpn. J. Ophthalmol. 51(6), 456–461 (2007).
[Crossref] [PubMed]

Bonci, D. M. O.

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

Bones, P. J.

Boppart, S. A.

N. D. Shemonski, F. A. South, Y.-Z. Liu, S. G. Adie, P. S. Carney, and S. A. Boppart, “Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9(7), 440–443 (2015).
[Crossref] [PubMed]

Bower, B. A.

Bowes Rickman, C.

Bradu, A.

Brainard, D. H.

Branham, K. E. H.

J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. H. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 48(7), 3283–3291 (2007).
[Crossref] [PubMed]

Bukowska, D. M.

Burgard, W.

A. Eitel, J. T. Springenberg, L. Spinello, M. Riedmiller, and W. Burgard, “Multimodal deep learning for robust RGB-D object recognition,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2015), 681–687.
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Burns, S. A.

Campbell, M.

Carney, P. S.

N. D. Shemonski, F. A. South, Y.-Z. Liu, S. G. Adie, P. S. Carney, and S. A. Boppart, “Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9(7), 440–443 (2015).
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Carroll, J.

K. M. Litts, R. F. Cooper, J. L. Duncan, and J. Carroll, “Photoreceptor-based biomarkers in AOSLO retinal imaging,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO255 (2017).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
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D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref] [PubMed]

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
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R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
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K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref] [PubMed]

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

C. Torti, B. Považay, B. Hofer, A. Unterhuber, J. Carroll, P. K. Ahnelt, and W. Drexler, “Adaptive optics optical coherence tomography at 120,000 depth scans/s for non-invasive cellular phenotyping of the living human retina,” Opt. Express 17(22), 19382–19400 (2009).
[Crossref] [PubMed]

J. Carroll, M. Neitz, H. Hofer, J. Neitz, and D. R. Williams, “Functional photoreceptor loss revealed with adaptive optics: An alternate cause of color blindness,” Proc. Natl. Acad. Sci. U.S.A. 101(22), 8461–8466 (2004).
[Crossref] [PubMed]

Chakraborty, D.

Chang, S.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Chatterjee, J.

Chen, F. K.

Chen, M.

Cheney, M. C.

J. Lammer, S. G. Prager, M. C. Cheney, A. Ahmed, S. H. Radwan, S. A. Burns, P. S. Silva, and J. K. Sun, “Cone photoreceptor irregularity on adaptive optics scanning laser ophthalmoscopy correlates with severity of diabetic retinopathy and macular edemacone mosaic irregularity in diabetic eyes on AOSLO,” Invest. Ophthalmol. Vis. Sci. 57(15), 6624–6632 (2016).
[Crossref] [PubMed]

Chew, A. L.

Chiang, J.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

Chiang, W.-C.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Chiu, S. J.

Choi, S.

Choi, S. S.

S. S. Choi, R. J. Zawadzki, M. A. Greiner, J. S. Werner, and J. L. Keltner, “Fourier-domain optical coherence tomography and adaptive optics reveal nerve fiber layer loss and photoreceptor changes in a patient with optic nerve drusen,” J. Neuroophthalmol. 28(2), 120–125 (2008).
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B. Xue, S. S. Choi, N. Doble, and J. S. Werner, “Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera,” J. Opt. Soc. Am. A 24(5), 1364–1372 (2007).
[Crossref] [PubMed]

S. S. Choi, N. Doble, J. L. Hardy, S. M. Jones, J. L. Keltner, S. S. Olivier, and J. S. Werner, “In vivo imaging of the photoreceptor mosaic in retinal dystrophies and correlations with visual function,” Invest. Ophthalmol. Vis. Sci. 47(5), 2080–2092 (2006).
[Crossref] [PubMed]

Chui, T. Y.

Chui, T. Y. P.

Chulay, J. D.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

Chung, M. M.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. 114(3), 586–591 (2017).
[Crossref] [PubMed]

Clarida, W.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Collison, F.

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

Collison, F. T.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

Conjeti, S.

Cooper, R. F.

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

K. M. Litts, R. F. Cooper, J. L. Duncan, and J. Carroll, “Photoreceptor-based biomarkers in AOSLO retinal imaging,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO255 (2017).
[Crossref] [PubMed]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

M. Chen, R. F. Cooper, G. K. Han, J. Gee, D. H. Brainard, and J. I. W. Morgan, “Multi-modal automatic montaging of adaptive optics retinal images,” Biomed. Opt. Express 7(12), 4899–4918 (2016).
[Crossref] [PubMed]

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
[Crossref] [PubMed]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref] [PubMed]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref] [PubMed]

Coram, M.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Cremers, F. P. M.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Cuadros, J.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Cunefare, D.

Curcio, C. A.

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref] [PubMed]

Dainty, C.

Davidson, B.

de Castro, A.

Deng, C.

Derby, J. C.

Devaney, N.

Dice, L. R.

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
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Doble, N.

B. Xue, S. S. Choi, N. Doble, and J. S. Werner, “Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera,” J. Opt. Soc. Am. A 24(5), 1364–1372 (2007).
[Crossref] [PubMed]

S. S. Choi, N. Doble, J. L. Hardy, S. M. Jones, J. L. Keltner, S. S. Olivier, and J. S. Werner, “In vivo imaging of the photoreceptor mosaic in retinal dystrophies and correlations with visual function,” Invest. Ophthalmol. Vis. Sci. 47(5), 2080–2092 (2006).
[Crossref] [PubMed]

Donnelly Iii, W.

Drexler, W.

Dubis, A. M.

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
[Crossref] [PubMed]

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
[Crossref] [PubMed]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref] [PubMed]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref] [PubMed]

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

Dubra, A.

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
[Crossref] [PubMed]

J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated photoreceptor cell identification on nonconfocal adaptive optics images using multiscale circular voting,” Invest. Ophthalmol. Vis. Sci. 58(11), 4477–4489 (2017).
[Crossref] [PubMed]

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

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref] [PubMed]

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref] [PubMed]

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
[Crossref] [PubMed]

D. Scoles, Y. N. Sulai, and A. Dubra, “In vivo dark-field imaging of the retinal pigment epithelium cell mosaic,” Biomed. Opt. Express 4(9), 1710–1723 (2013).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
[Crossref] [PubMed]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref] [PubMed]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref] [PubMed]

A. Dubra and Y. Sulai, “Reflective afocal broadband adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(6), 1757–1768 (2011).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref] [PubMed]

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

Duncan, J. L.

K. M. Litts, R. F. Cooper, J. L. Duncan, and J. Carroll, “Photoreceptor-based biomarkers in AOSLO retinal imaging,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO255 (2017).
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A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu Rev Vis Sci 1(1), 19–50 (2015).
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D. Merino, J. L. Duncan, P. Tiruveedhula, and A. Roorda, “Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(8), 2189–2201 (2011).
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J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. H. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 48(7), 3283–3291 (2007).
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Eitel, A.

A. Eitel, J. T. Springenberg, L. Spinello, M. Riedmiller, and W. Burgard, “Multimodal deep learning for robust RGB-D object recognition,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2015), 681–687.
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Erginay, A.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Erker, L. R.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
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Fang, L.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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Farsiu, S.

Fei, X.

Fei-Fei, L.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, (IEEE, 2014), 1725–1732.
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Feng, B.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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Ferguson, R. D.

Fischer, W.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. 114(3), 586–591 (2017).
[Crossref] [PubMed]

Fishman, G. A.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Vis. Sci. 57(10), 3984–3995 (2016).
[Crossref] [PubMed]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref] [PubMed]

A. M. Dubis, R. F. Cooper, J. Aboshiha, C. S. Langlo, V. Sundaram, B. Liu, F. Collison, G. A. Fishman, A. T. Moore, A. R. Webster, A. Dubra, J. Carroll, and M. Michaelides, “Genotype-dependent variability in residual cone structure in achromatopsia: Toward developing metrics for assessing cone health,” Invest. Ophthalmol. Vis. Sci. 55(11), 7303–7311 (2014).
[Crossref] [PubMed]

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

Folk, J. C.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
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Fu, H.

H. Fu, Y. Xu, S. Lin, D. W. Kee Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer International Publishing, 2016), 132–139.
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Fujikado, T.

Y. Kitaguchi, S. Kusaka, T. Yamaguchi, T. Mihashi, and T. Fujikado, “Detection of photoreceptor disruption by adaptive optics fundus imaging and fourier-domain optical coherence tomography in eyes with occult macular dystrophy,” Clin. Ophthalmol. 5, 345–351 (2011).
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Y. Kitaguchi, K. Bessho, T. Yamaguchi, N. Nakazawa, T. Mihashi, and T. Fujikado, “In vivo measurements of cone photoreceptor spacing in myopic eyes from images obtained by an adaptive optics fundus camera,” Jpn. J. Ophthalmol. 51(6), 456–461 (2007).
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Gandhi, J.

J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. H. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 48(7), 3283–3291 (2007).
[Crossref] [PubMed]

Gao, W.

Garcia Garrido, M.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
[Crossref] [PubMed]

Garrioch, R.

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref] [PubMed]

Gee, J.

Genead, M. A.

M. A. Genead, G. A. Fishman, J. Rha, A. M. Dubis, D. M. O. Bonci, A. Dubra, E. M. Stone, M. Neitz, and J. Carroll, “Photoreceptor structure and function in patients with congenital achromatopsia,” Invest. Ophthalmol. Vis. Sci. 52(10), 7298–7308 (2011).
[Crossref] [PubMed]

Gonzalez Menendez, I.

S. Kohl, D. Zobor, W.-C. Chiang, N. Weisschuh, J. Staller, I. Gonzalez Menendez, S. Chang, S. C. Beck, M. Garcia Garrido, V. Sothilingam, M. W. Seeliger, F. Stanzial, F. Benedicenti, F. Inzana, E. Héon, A. Vincent, J. Beis, T. M. Strom, G. Rudolph, S. Roosing, A. I. Hollander, F. P. M. Cremers, I. Lopez, H. Ren, A. T. Moore, A. R. Webster, M. Michaelides, R. K. Koenekoop, E. Zrenner, R. J. Kaufman, S. H. Tsang, B. Wissinger, and J. H. Lin, “Mutations in the unfolded protein response regulator ATF6 cause the cone dysfunction disorder achromatopsia,” Nat. Genet. 47(7), 757–765 (2015).
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Götzinger, E.

Granger, C. E.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. 114(3), 586–591 (2017).
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Greiner, M. A.

S. S. Choi, R. J. Zawadzki, M. A. Greiner, J. S. Werner, and J. L. Keltner, “Fourier-domain optical coherence tomography and adaptive optics reveal nerve fiber layer loss and photoreceptor changes in a patient with optic nerve drusen,” J. Neuroophthalmol. 28(2), 120–125 (2008).
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Figures (8)

Fig. 1
Fig. 1 Dual-mode AOSLO cone imaging in ACHM subjects. (a) Split detector AOSLO image near the fovea of an ACHM subject. (b) Simultaneously captured confocal AOSLO image from the same location as (a). (c) Split detector AOSLO image at 12° from the fovea in another subject with ACHM. (d) Simultaneously captured confocal AOSLO image from the same location as (c). Orange arrows point to ambiguous locations in the split detector image (c) that can be seen to be cones based on the dark circles in the confocal image (d). Scale bars: 20 μm.
Fig. 2
Fig. 2 Schematic of the dual-mode CNN AOSLO cone detection algorithm.
Fig. 3
Fig. 3 Extraction of labeled patches from AOSLO image pairs. (a) Cropped split detector AOSLO image. (b) Simultaneously captured cropped confocal AOSLO image from the same location. Voronoi diagram overlain in cyan, manually marked cones are shown in green, and randomly generated locations along Voronoi edges are shown in yellow. (c) Example cone patch pair from position shown in purple in (a) and (b). (d) Example non-cone patch pair from position shown in red in (a) and (b).
Fig. 4
Fig. 4 Proposed late fusion dual-mode CNN (LF-DM-CNN) architecture, which consists of the following layers: convolutional (Conv(N,F) where N is the number of kernels, and F is the kernel size in the first two dimensions), fully connected (FC(X) where X is the number of output nodes) batch normalization (BatchNorm), max pooling (MaxPool), average pooling (AvePool), ReLu, concatenation, and soft-max.
Fig. 5
Fig. 5 Filter weights from the first convolutional layer in the LF-DM-CNN for the (a) split detector and (b) confocal paths.
Fig. 6
Fig. 6 Detection of cones in split detector and confocal AOSLO image pairs. (a) Split detector AOSLO image. (b) Simultaneously captured confocal AOSLO image from the same location. (c) Probability maps generated from (a) and (b) using the trained LF-DM-CNN. (d) Extended maxima of (c). (e-f) Detected cones marked in green on the split detector image shown in (a) and on the confocal image shown in (b).
Fig. 7
Fig. 7 Performance of the automated cone detection algorithms on an ACHM image pair. (a) Split detector AOSLO image. (b) Simultaneously captured confocal AOSLO image from the same location. (c-i) Comparison to the first manual markings (with Dice’s coefficients) for (c) the second manual markings (0.915), (d) Bergeles et al. [48] (0.667), (e) C-CNN [46] (0.178), (f) SD-CNN [46] (0.800), (g) C-CNN-ACHM (0.835), (h) SD-CNN-ACHM (0.907), and (i) our proposed method using the LF-DM-CNN network (0.932). Green points denote true positives, cyan denotes false negatives, and red denotes false positives.
Fig. 8
Fig. 8 Comparison of our dual-mode method to the single-mode Cunefare et al. [46] method with the SD-CNN-ACHM. Split detector AOSLO images from different subjects with ACHM are shown in the top row, and the corresponding simultaneously captured confocal AOSLO images are shown in the row second from the top. Comparisons to the first manual markings for the single-mode SD-CNN-ACHM are shown in the second row from the bottom, and our method using the dual-mode LF-DM-CNN are shown in the bottom row. Green points denote true positives, cyan denotes false negatives, and red denotes false positives. Orange arrows point to ambiguous locations in the split detector images. Dice’s coefficients for the SD-CNN-ACHM are 0.914 in (a), 0.867 in (b), and 0.815 in (c). Dice’s coefficients for the LF-DM-CNN are 0.986 in (a), 0.929 in (b), and 0.897 in (c).

Tables (1)

Tables Icon

Table 1 Average performance of automatic methods and second manual marking with respect to the first manual marking across the data set (standard deviations shown in parenthesis).

Equations (5)

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

N Automatic = N TP + N FP ,
N Manual = N TP + N FN ,
True positive rate= N TP / N Manual ,
False discovery rate= N FP / N Automatic ,
Dice'scoefficient= 2N TP / (N Manual + N Automatic ).

Metrics