Abstract

Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.

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

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2018 (4)

H. Song, E. A. Rossi, E. Stone, L. Latchney, D. Williams, A. Dubra, and M. Chung, “Phenotypic diversity in autosomal-dominant cone-rod dystrophy elucidated by adaptive optics retinal imaging,” The Br. journal ophthalmology 102, 136–141 (2018).
[Crossref]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

M. Salas, M. Augustin, F. Felberer, A. Wartak, M. Laslandes, L. Ginner, M. Niederleithner, J. Ensher, M. P. Minneman, R. A. Leitgeb, W. Drexler, X. Levecq, U. Schmidt-Erfurth, and M. Pircher, “Compact akinetic swept source optical coherence tomography angiography at 1060 nm supporting a wide field of view and adaptive optics imaging modes of the posterior eye,” Biomed. Opt. Express 9, 1871 (2018).
[Crossref] [PubMed]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9, 3740–3756 (2018).
[Crossref]

2017 (7)

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, 579–592 (2017).
[Crossref] [PubMed]

M. Pircher and R. J. Zawadzki, “Review of adaptive optics oct (ao-oct): principles and applications for retinal imaging [invited],” Biomed. Opt. Express 8, 2536–2562 (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, 2732–2744 (2017).
[Crossref] [PubMed]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, and J. Carroll, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Reports 76620 (2017).

A. Lee, P. Taylor, J. Kalpathy-Cramer, and A. Tufail, “Machine learning has arrived!” Ophthalmology 124, 1726–1728 (2017).
[Crossref] [PubMed]

M. Heisler, S. Lee, Z. Mammo, Y. Jian, M. Ju, A. Merkur, E. Navajas, C. Balaratnasingam, M. F. Beg, and M. V. Sarunic, “Strip-based registration of serially acquired optical coherence tomography angiography Strip-based registration of serially acquired optical,” J. Biomed. Opt. 22, 036007 (2017).
[Crossref]

M. J. Ju, M. Heisler, D. Wahl, Y. Jian, and M. V. Sarunic, “Multiscale sensorless adaptive optics OCT angiography system for in vivo human retinal imaging,” J. Biomed. Opt. 22, 1 (2017).
[Crossref] [PubMed]

2016 (7)

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Investig. Ophthalmol. & Vis. Sci. 57, 2992 (2016).
[Crossref]

P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Transactions on Med. Imaging 35, 2369–2380 (2016).
[Crossref]

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 Transactions on Med. Imaging 35, 109–118 (2016).
[Crossref]

V Ga, L Pa, M Ca, and et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316, 2402–2410 (2016).
[Crossref]

P. Prentašic, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Loncaric, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21, 075008 (2016).
[Crossref]

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, 2036 (2016).
[Crossref] [PubMed]

O. P. Kocaoglu, Z. Liu, F. Zhang, K. Kurokawa, R. S. Jonnal, and D. T. Miller, “Photoreceptor disc shedding in the living human eye,” Biomed. Opt. Express 7, 4554 (2016).
[Crossref] [PubMed]

2015 (2)

A. Nakanishi, S. Ueno, K. Kawano, Y. Ito, T. Kominami, S. Yasuda, M. Kondo, K. Tsunoda, T. Iwata, and H. Terasaki, “Pathologic changes of cone photoreceptors in eyes with occult macular dystrophy,” Investig. Ophthalmol. Vis. Sci. 56, 7243–7249 (2015).
[Crossref]

M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science 349, 255–260 (2015).
[Crossref] [PubMed]

2014 (1)

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
[Crossref] [PubMed]

2013 (3)

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. optics express 4, 924–937 (2013).
[Crossref]

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, 540–549 (2013).
[Crossref] [PubMed]

Y. Makiyama, S. Ooto, M. Hangai, K. Takayama, A. Uji, A. Oishi, K. Ogino, S. Nakagawa, and N. Yoshimura, “Macular cone abnormalities in retinitis pigmentosa with preserved central vision using adaptive optics scanning laser ophthalmoscopy,” PLoS ONE 8e79447 (2013).
[Crossref] [PubMed]

2012 (1)

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, 632–643 (2012).
[Crossref] [PubMed]

2011 (8)

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).
[Crossref] [PubMed]

D. R. Williams, “Imaging single cells in the living retina,” Vis. Res. 51, 1379–1396 (2011). Vision Research 50th Anniversary Issue: Part 2.
[Crossref] [PubMed]

K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Investig. ophthalmology & visual science 52, 2219–2226 (2011).
[Crossref]

H. Song, T. Y. P. Chui, Z. Zhong, A. E. Elsner, and S. A. Burns, “Variation of cone photoreceptor packing density with retinal eccentricity and age,” Investig. Ophthalmol. & Vis. Sci. 52, 7376 (2011).
[Crossref]

S. Ooto, M. Hangai, K. Takayama, A. Sakamoto, A. Tsujikawa, S. Oshima, T. Inoue, and N. Yoshimura, “High-resolution imaging of the photoreceptor layer in epiretinal membrane using adaptive optics scanning laser ophthal-moscopy,” Ophthalmology. 118, 873–881 (2011).
[Crossref]

O. P. Kocaoglu, S. Lee, R. S. Jonnal, Q. Wang, a. E. Herde, J. C. Derby, W. H. 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, 748–763 (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, 1864 (2011).
[Crossref] [PubMed]

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, 2189–2201 (2011).
[Crossref] [PubMed]

2008 (3)

D. H. Wojtas, B. Wu, P. K. Ahnelt, P. J. Bones, and R. P. Millane, “Automated analysis of differential interference contrast microscopy images of the foveal cone mosaic,” J. Opt. Soc. Am. A 25, 1181–1189 (2008).
[Crossref]

T. Y. Chui, H. Song, and S. a. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A, Opt. image science, vision 25, 3021–3029 (2008).
[Crossref]

M. Pircher, R. J. Zawadzki, J. W. Evans, J. S. Werner, and C. K. Hitzenberger, “Simultaneous imaging of human cone mosaic with adaptive optics enhanced scanning laser ophthalmoscopy and high-speed transversal scanning optical coherence tomography,” Opt. letters 33, 22–24 (2008).
[Crossref]

2007 (4)

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

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

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, Opt. image science, vision 24, 1364–1372 (2007).
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K. Y. Li and A. Roorda, “Automated identification of cone photoreceptors in adaptive optics retinal images,” J. Opt. Soc. Am. A 24, 1358–1363 (2007).
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2006 (2)

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,” Investig. Ophthalmol. & Vis. Sci. 47, 2080 (2006).
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J. I. Wolfing, M. Chung, J. Carroll, A. Roorda, and D. R. Williams, “High-Resolution Retinal Imaging of Cone-Rod Dystrophy,” Ophthalmology. 1131014 (2006).
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2004 (1)

2002 (1)

1997 (1)

1990 (1)

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).

1979 (1)

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D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Beg, M. F.

M. Heisler, S. Lee, Z. Mammo, Y. Jian, M. Ju, A. Merkur, E. Navajas, C. Balaratnasingam, M. F. Beg, and M. V. Sarunic, “Strip-based registration of serially acquired optical coherence tomography angiography Strip-based registration of serially acquired optical,” J. Biomed. Opt. 22, 036007 (2017).
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Blau, S.

Bones, P. J.

Branham, K. E.

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

H. Song, T. Y. P. Chui, Z. Zhong, A. E. Elsner, and S. A. Burns, “Variation of cone photoreceptor packing density with retinal eccentricity and age,” Investig. Ophthalmol. & Vis. Sci. 52, 7376 (2011).
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T. Y. Chui, H. Song, and S. a. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A, Opt. image science, vision 25, 3021–3029 (2008).
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Ca, M

V Ga, L Pa, M Ca, and et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316, 2402–2410 (2016).
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Cai, W.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Campbell, M. C.

Carlsson, S.

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: An astounding baseline for recognition,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, (2014), pp. 512–519.
[Crossref]

Carroll, J.

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9, 3740–3756 (2018).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, and J. Carroll, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Reports 76620 (2017).

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, 2036 (2016).
[Crossref] [PubMed]

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Investig. Ophthalmol. & Vis. Sci. 57, 2992 (2016).
[Crossref]

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, 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. optics express 4, 924–937 (2013).
[Crossref]

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, 632–643 (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, 1864 (2011).
[Crossref] [PubMed]

J. I. Wolfing, M. Chung, J. Carroll, A. Roorda, and D. R. Williams, “High-Resolution Retinal Imaging of Cone-Rod Dystrophy,” Ophthalmology. 1131014 (2006).
[Crossref] [PubMed]

Chakraborty, D.

Chatterjee, J.

Chen, F. K.

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
[Crossref] [PubMed]

Chiu, S. J.

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. optics express 4, 924–937 (2013).
[Crossref]

Choi, S. S.

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, Opt. image science, vision 24, 1364–1372 (2007).
[Crossref]

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,” Investig. Ophthalmol. & Vis. Sci. 47, 2080 (2006).
[Crossref]

Chui, T. Y.

T. Y. Chui, H. Song, and S. a. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A, Opt. image science, vision 25, 3021–3029 (2008).
[Crossref]

Chui, T. Y. P.

H. Song, T. Y. P. Chui, Z. Zhong, A. E. Elsner, and S. A. Burns, “Variation of cone photoreceptor packing density with retinal eccentricity and age,” Investig. Ophthalmol. & Vis. Sci. 52, 7376 (2011).
[Crossref]

Chung, M.

H. Song, E. A. Rossi, E. Stone, L. Latchney, D. Williams, A. Dubra, and M. Chung, “Phenotypic diversity in autosomal-dominant cone-rod dystrophy elucidated by adaptive optics retinal imaging,” The Br. journal ophthalmology 102, 136–141 (2018).
[Crossref]

J. I. Wolfing, M. Chung, J. Carroll, A. Roorda, and D. R. Williams, “High-Resolution Retinal Imaging of Cone-Rod Dystrophy,” Ophthalmology. 1131014 (2006).
[Crossref] [PubMed]

Clune, J.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, (MIT Press, Cambridge, MA, USA, 2014), NIPS’14, pp. 3320–3328.

Coffey, P. J.

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
[Crossref] [PubMed]

Cooper, R. F.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, and J. Carroll, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Reports 76620 (2017).

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, 2036 (2016).
[Crossref] [PubMed]

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Investig. Ophthalmol. & Vis. Sci. 57, 2992 (2016).
[Crossref]

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, 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, 632–643 (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, 1864 (2011).
[Crossref] [PubMed]

Cruz, L. Da

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
[Crossref] [PubMed]

Cunefare, D.

Curcio, C. A.

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).

Da, W. J.

Darrell, T.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” CoRR abs/1310.1531 (2013).

Derby, J. C.

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, Opt. image science, vision 24, 1364–1372 (2007).
[Crossref]

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,” Investig. Ophthalmol. & Vis. Sci. 47, 2080 (2006).
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Donahue, J.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” CoRR abs/1310.1531 (2013).

Dong, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Drexler, W.

Duan, Y.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Dubis, A. M.

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. optics express 4, 924–937 (2013).
[Crossref]

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, 632–643 (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, 1864 (2011).
[Crossref] [PubMed]

Dubra, A.

H. Song, E. A. Rossi, E. Stone, L. Latchney, D. Williams, A. Dubra, and M. Chung, “Phenotypic diversity in autosomal-dominant cone-rod dystrophy elucidated by adaptive optics retinal imaging,” The Br. journal ophthalmology 102, 136–141 (2018).
[Crossref]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9, 3740–3756 (2018).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, and J. Carroll, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Reports 76620 (2017).

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, 2036 (2016).
[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, 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. optics express 4, 924–937 (2013).
[Crossref]

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, 632–643 (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, 1864 (2011).
[Crossref] [PubMed]

Duncan, J. L.

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, 2189–2201 (2011).
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K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Investig. ophthalmology & visual science 52, 2219–2226 (2011).
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J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Investig. Ophthalmol. Vis. Sci. 48, 3283–3291 (2007).
[Crossref]

Elsner, A. E.

H. Song, T. Y. P. Chui, Z. Zhong, A. E. Elsner, and S. A. Burns, “Variation of cone photoreceptor packing density with retinal eccentricity and age,” Investig. Ophthalmol. & Vis. Sci. 52, 7376 (2011).
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Ensher, J.

Evans, J. W.

M. Pircher, R. J. Zawadzki, J. W. Evans, J. S. Werner, and C. K. Hitzenberger, “Simultaneous imaging of human cone mosaic with adaptive optics enhanced scanning laser ophthalmoscopy and high-speed transversal scanning optical coherence tomography,” Opt. letters 33, 22–24 (2008).
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Fang, L.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, and J. Carroll, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Reports 76620 (2017).

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, 2732–2744 (2017).
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Farsiu, S.

Felberer, F.

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 Transactions on Med. Imaging 35, 109–118 (2016).
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Fercher, A. F.

Fernández, E. J.

Fu, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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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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Ga, V

V Ga, L Pa, M Ca, and et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316, 2402–2410 (2016).
[Crossref]

Gandhi, J.

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

Gao, W. H.

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, 632–643 (2012).
[Crossref] [PubMed]

Gias, C.

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
[Crossref] [PubMed]

Ginner, L.

Goldbaum, M.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Guymer, R. H.

Hangai, M.

Y. Makiyama, S. Ooto, M. Hangai, K. Takayama, A. Uji, A. Oishi, K. Ogino, S. Nakagawa, and N. Yoshimura, “Macular cone abnormalities in retinitis pigmentosa with preserved central vision using adaptive optics scanning laser ophthalmoscopy,” PLoS ONE 8e79447 (2013).
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D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Xie, L.

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 Transactions on Med. Imaging 35, 109–118 (2016).
[Crossref]

Xu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Xue, B.

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, Opt. image science, vision 24, 1364–1372 (2007).
[Crossref]

Yamaguchi, 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).
[Crossref] [PubMed]

Yan, F.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Yang, G.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Yasuda, S.

A. Nakanishi, S. Ueno, K. Kawano, Y. Ito, T. Kominami, S. Yasuda, M. Kondo, K. Tsunoda, T. Iwata, and H. Terasaki, “Pathologic changes of cone photoreceptors in eyes with occult macular dystrophy,” Investig. Ophthalmol. Vis. Sci. 56, 7243–7249 (2015).
[Crossref]

Yoshimura, N.

Y. Makiyama, S. Ooto, M. Hangai, K. Takayama, A. Uji, A. Oishi, K. Ogino, S. Nakagawa, and N. Yoshimura, “Macular cone abnormalities in retinitis pigmentosa with preserved central vision using adaptive optics scanning laser ophthalmoscopy,” PLoS ONE 8e79447 (2013).
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S. Ooto, M. Hangai, K. Takayama, A. Sakamoto, A. Tsujikawa, S. Oshima, T. Inoue, and N. Yoshimura, “High-resolution imaging of the photoreceptor layer in epiretinal membrane using adaptive optics scanning laser ophthal-moscopy,” Ophthalmology. 118, 873–881 (2011).
[Crossref]

Yosinski, J.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, (MIT Press, Cambridge, MA, USA, 2014), NIPS’14, pp. 3320–3328.

Zawadzki, R. J.

M. Pircher and R. J. Zawadzki, “Review of adaptive optics oct (ao-oct): principles and applications for retinal imaging [invited],” Biomed. Opt. Express 8, 2536–2562 (2017).
[Crossref] [PubMed]

M. Pircher, R. J. Zawadzki, J. W. Evans, J. S. Werner, and C. K. Hitzenberger, “Simultaneous imaging of human cone mosaic with adaptive optics enhanced scanning laser ophthalmoscopy and high-speed transversal scanning optical coherence tomography,” Opt. letters 33, 22–24 (2008).
[Crossref]

Zhang, C. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Zhang, E. D.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
[Crossref] [PubMed]

Zhang, F.

Zhang, H.

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 Transactions on Med. Imaging 35, 109–118 (2016).
[Crossref]

Zhang, K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Zhang, N.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” CoRR abs/1310.1531 (2013).

Zhang, R.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Zhang, Y.

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

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Zhong, J.

M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
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Zhong, Z.

H. Song, T. Y. P. Chui, Z. Zhong, A. E. Elsner, and S. A. Burns, “Variation of cone photoreceptor packing density with retinal eccentricity and age,” Investig. Ophthalmol. & Vis. Sci. 52, 7376 (2011).
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Zhu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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Ziyar, I.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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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, 2536–2562 (2017).
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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, 2189–2201 (2011).
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O. P. Kocaoglu, Z. Liu, F. Zhang, K. Kurokawa, R. S. Jonnal, and D. T. Miller, “Photoreceptor disc shedding in the living human eye,” Biomed. Opt. Express 7, 4554 (2016).
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M. Salas, M. Augustin, F. Felberer, A. Wartak, M. Laslandes, L. Ginner, M. Niederleithner, J. Ensher, M. P. Minneman, R. A. Leitgeb, W. Drexler, X. Levecq, U. Schmidt-Erfurth, and M. Pircher, “Compact akinetic swept source optical coherence tomography angiography at 1060 nm supporting a wide field of view and adaptive optics imaging modes of the posterior eye,” Biomed. Opt. Express 9, 1871 (2018).
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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, 1864 (2011).
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O. P. Kocaoglu, S. Lee, R. S. Jonnal, Q. Wang, a. E. Herde, J. C. Derby, W. H. 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, 748–763 (2011).
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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, 2036 (2016).
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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, 579–592 (2017).
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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, 2732–2744 (2017).
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D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9, 3740–3756 (2018).
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M. N. Muthiah, C. Gias, F. K. Chen, J. Zhong, Z. McClelland, F. B. Sallo, T. Peto, P. J. Coffey, and L. Da Cruz, “Cone photoreceptor definition on adaptive optics retinal imaging,” Br. J. Ophthalmol. 98, 1073–1079 (2014).
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Cell (1)

D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172, 1122–1124 (2018).
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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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J. L. Duncan, Y. Zhang, J. Gandhi, C. Nakanishi, M. Othman, K. E. Branham, A. Swaroop, and A. Roorda, “High-resolution imaging with adaptive optics in patients with inherited retinal degeneration,” Investig. Ophthalmol. Vis. Sci. 48, 3283–3291 (2007).
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A. Nakanishi, S. Ueno, K. Kawano, Y. Ito, T. Kominami, S. Yasuda, M. Kondo, K. Tsunoda, T. Iwata, and H. Terasaki, “Pathologic changes of cone photoreceptors in eyes with occult macular dystrophy,” Investig. Ophthalmol. Vis. Sci. 56, 7243–7249 (2015).
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K. Y. Li and A. Roorda, “Automated identification of cone photoreceptors in adaptive optics retinal images,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1358–1363 (2007).
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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, Opt. image science, vision 24, 1364–1372 (2007).
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Figures (7)

Fig. 1
Fig. 1 Data acquired from a 26 year old female control subject at retinal eccentricities of (a–d) ∼3.5°, ∼5°, ∼6.5°, and ∼8° respectively. Scalebar 50 μm.
Fig. 2
Fig. 2 An original AO-OCT image taken at ∼6.5° retinal eccentricity is displayed in (a), and the center of the cones (magenta) and Voronoi map (green) is overlaid onto the image in (b). In (c) the Voronoi cells are shaded based on the number of neighbours, and in (d) the cells are shaded based on their area. Scalebar 50 μm.
Fig. 3
Fig. 3 Comparison of automated results in (a) an AO-OCT image from the (b) transfer learning, (c) fine-tuning (Layer 5) and (d) fine-tuning (Layer 9) methods. In the marked images, a green point indicates an automatically detected cone that was matched to a manually marked cone (true positive), a yellow point indicates a cone missed by the automatic algorithm (false negative), and a red point indicates an automatic marking with no corresponding manually marked cone (false positive). Scalebar 50 μm.
Fig. 4
Fig. 4 Comparison of manual results in two AO-OCT images (a) and (e) to a second rater (b,f) and the CNN (c–d,g–h). In the manually marked comparison images (b,f), a green point indicates a cone marked by Rater A that was matched to a cone marked by Rater B (true positive), a yellow point indicates a cone missed by Rater B (false negative), and a red point indicates a marking by Rater B with no corresponding cone marked by Rater A (false positive). In the comparison images to the CNN (c–d,g–h), a green point indicates a cone marked by a Rater that was matched to a cone marked by the CNN (true positive), a yellow point indicates a cone missed by the CNN (false negative), and a red point indicates a marking by the CNN with no corresponding cone marked by the Rater (false positive). Scalebar 50 μm.
Fig. 5
Fig. 5 How the number of training patches affects the performance of the different CNN algorithms. Random weight initialization (Random) is comparable to the Transfer Learning (TFL), Fine-Tuning Layer 5 (FT5), and Fine-Tuning Layer 9 (FT9) methods at 3,000 training patches.
Fig. 6
Fig. 6 Comparison of cone density measurements from the Literature to the cone density measurements from the AO-OCT system. The gold standard of histology [49] is shown with a trendline, as well as measurements from two AO-SLO systems [50,51].
Fig. 7
Fig. 7 Results from a 22 year old male subject. Colocalization of the Areas 1 and 2 to a (a) widefield OCT Bscan [scalebar 100 μm] and (b) widefield AO-OCT-A en face view [scalebar 100 μm] are shown. The original AO-OCT images from Areas 1–4 are shown in (c–f) [scalebar 50 μm], automated segmentation results and the Voronoi diagrams are in (g–j), the Voronoi cells in (k–n) are shaded based on the number of neighbours, and in (o–r) the cells are shaded based on their area.

Tables (4)

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Table 1 CNN Architecture

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Table 2 Average performance of the automated methods with respect to manual marking

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Table 3 Average performance of both raters and the automated methods with respect to manual marking

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Table 4 Cone Mosaic Measurements by Area

Equations (3)

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Dice s Coefficient = 2 TP TP + FN + TP + FP
Sensitivity = TP TP + FN
False Discovery Rate = FP TP + FP