Abstract

Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations of clinically relevant image features.

© 2017 Optical Society of America

Full Article  |  PDF Article
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2017 (2)

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (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]

2016 (6)

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

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(7), 075008 (2016).
[Crossref] [PubMed]

P. Korfiatis, T. L. Kline, and B. J. Erickson, “Automated segmentation of hyperintense regions in FLAIR MRI using deep learning,” Tomography 2(4), 334–340 (2016).
[Crossref] [PubMed]

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

M. Esmaeili, A. M. Dehnavi, H. Rabbani, and F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD,” J. Med. Signals Sens. 6(3), 166–171 (2016).
[PubMed]

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
[Crossref] [PubMed]

2015 (4)

2014 (1)

J. B. Mitchell, “Machine learning methods in chemoinformatics,” Wiley Interdiscip. Rev. Comput. Mol. Sci. 4(5), 468–481 (2014).
[Crossref] [PubMed]

2013 (2)

Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
[Crossref] [PubMed]

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
[Crossref] [PubMed]

2011 (3)

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

P. A. Keane and S. R. Sadda, “Predicting visual outcomes for macular disease using optical coherence tomography,” Saudi J. Ophthalmol. 25(2), 145–158 (2011).
[Crossref] [PubMed]

A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
[Crossref] [PubMed]

2010 (1)

G. Coscas, J. Cunha-Vaz, and G. Soubrane, “Macular edema: definition and basic concepts,” Dev. Ophthalmol. 47, 1–9 (2010).
[Crossref] [PubMed]

2007 (1)

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

2005 (1)

J. Sahni, P. Stanga, D. Wong, and S. Harding, “Optical coherence tomography in photodynamic therapy for subfoveal choroidal neovascularisation secondary to age related macular degeneration: a cross sectional study,” Br. J. Ophthalmol. 89(3), 316–320 (2005).
[Crossref] [PubMed]

1994 (1)

A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Trans. Med. Imaging 13(4), 716–724 (1994).
[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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Ahlers, C.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Aiello, L. P.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Allingham, M. J.

Al-Louzi, O.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, S. Saidha, and J. L. Prince, Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study, Proc SPIE Int Soc Opt Eng. 9417 (2015).

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Apte, R. S.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Araie, M.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Asaoka, R.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Baughman, D. M.

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images,” Ophthalmology Retina. In press.

Beck, R. W.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Beg, M. F.

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(7), 075008 (2016).
[Crossref] [PubMed]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Bhargava, P.

Bhavsar, A. R.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Bilgin, G.

N. Hatipoglu and G. Bilgin, “Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships,” Med. Biol. Eng. Comput. (2017).

Bressler, N. M.

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Brown, D. M.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Browning, D. J.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Burlina, P.

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

Calabresi, P. A.

Campa, C.

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
[Crossref] [PubMed]

A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
[Crossref] [PubMed]

Carass, A.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, S. Saidha, and J. L. Prince, Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study, Proc SPIE Int Soc Opt Eng. 9417 (2015).

Chiu, S. J.

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Coscas, G.

G. Coscas, J. Cunha-Vaz, and G. Soubrane, “Macular edema: definition and basic concepts,” Dev. Ophthalmol. 47, 1–9 (2010).
[Crossref] [PubMed]

Cousins, S. W.

Cunefare, D.

Cunha-Vaz, J.

G. Coscas, J. Cunha-Vaz, and G. Soubrane, “Macular edema: definition and basic concepts,” Dev. Ophthalmol. 47, 1–9 (2010).
[Crossref] [PubMed]

Danis, R. P.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Dawant, B. M.

A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Trans. Med. Imaging 13(4), 716–724 (1994).
[Crossref] [PubMed]

Dehnavi, A. M.

M. Esmaeili, A. M. Dehnavi, H. Rabbani, and F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD,” J. Med. Signals Sens. 6(3), 166–171 (2016).
[PubMed]

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Erickson, B. J.

P. Korfiatis, T. L. Kline, and B. J. Erickson, “Automated segmentation of hyperintense regions in FLAIR MRI using deep learning,” Tomography 2(4), 334–340 (2016).
[Crossref] [PubMed]

Esmaeili, M.

M. Esmaeili, A. M. Dehnavi, H. Rabbani, and F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD,” J. Med. Signals Sens. 6(3), 166–171 (2016).
[PubMed]

Fang, L.

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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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
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D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
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P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
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X. Gao, S. Lin, and T. Y. Wong, “Automatic feature learning to grade nuclear cataracts based on deep learning,” IEEE Trans. Biomed. Eng. 62(11), 2693–2701 (2015).
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S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Glassman, A. R.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
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I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
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D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
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Hajizadeh, F.

M. Esmaeili, A. M. Dehnavi, H. Rabbani, and F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD,” J. Med. Signals Sens. 6(3), 166–171 (2016).
[PubMed]

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J. Sahni, P. Stanga, D. Wong, and S. Harding, “Optical coherence tomography in photodynamic therapy for subfoveal choroidal neovascularisation secondary to age related macular degeneration: a cross sectional study,” Br. J. Ophthalmol. 89(3), 316–320 (2005).
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Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
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A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
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Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
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S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

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N. Hatipoglu and G. Bilgin, “Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships,” Med. Biol. Eng. Comput. (2017).

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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(7), 075008 (2016).
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Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
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Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
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Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
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Hwang, T. S.

Ip, M.

S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Iwase, A.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
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Izatt, J. A.

Jia, Y.

Joshi, N.

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

Keane, P. A.

P. A. Keane and S. R. Sadda, “Predicting visual outcomes for macular disease using optical coherence tomography,” Saudi J. Ophthalmol. 25(2), 145–158 (2011).
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Kinyoun, J. L.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
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P. Korfiatis, T. L. Kline, and B. J. Erickson, “Automated segmentation of hyperintense regions in FLAIR MRI using deep learning,” Tomography 2(4), 334–340 (2016).
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Korfiatis, P.

P. Korfiatis, T. L. Kline, and B. J. Erickson, “Automated segmentation of hyperintense regions in FLAIR MRI using deep learning,” Tomography 2(4), 334–340 (2016).
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Kumar, A.

A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
[Crossref] [PubMed]

Lang, A.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, S. Saidha, and J. L. Prince, Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study, Proc SPIE Int Soc Opt Eng. 9417 (2015).

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
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C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images,” Ophthalmology Retina. In press.

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C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images,” Ophthalmology Retina. In press.

Lee, S.

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(7), 075008 (2016).
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Li, D.

Li, S.

Lim, J. I.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Lin, S.

X. Gao, S. Lin, and T. Y. Wong, “Automatic feature learning to grade nuclear cataracts based on deep learning,” IEEE Trans. Biomed. Eng. 62(11), 2693–2701 (2015).
[Crossref] [PubMed]

Liu, L.

Loncaric, S.

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(7), 075008 (2016).
[Crossref] [PubMed]

Lou, Y.

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Mammo, Z.

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(7), 075008 (2016).
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A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Trans. Med. Imaging 13(4), 716–724 (1994).
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Medioni, G. G.

Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
[Crossref] [PubMed]

Merkur, A.

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(7), 075008 (2016).
[Crossref] [PubMed]

Mettu, P. S.

Miskala, P. H.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
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J. B. Mitchell, “Machine learning methods in chemoinformatics,” Wiley Interdiscip. Rev. Comput. Mol. Sci. 4(5), 468–481 (2014).
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R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Navajas, E.

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(7), 075008 (2016).
[Crossref] [PubMed]

Nguyen, Q. D.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Niemeijer, M.

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 data set through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Nittala, M. G.

S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Pacheco, K. D.

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

Palmer, A. C.

A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Trans. Med. Imaging 13(4), 716–724 (1994).
[Crossref] [PubMed]

Pechauer, A. D.

Pfau, M.

S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Pieramici, D. J.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Prentašic, P.

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(7), 075008 (2016).
[Crossref] [PubMed]

Prince, J. L.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, S. Saidha, and J. L. Prince, Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study, Proc SPIE Int Soc Opt Eng. 9417 (2015).

Prünte, C.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Rabbani, H.

M. Esmaeili, A. M. Dehnavi, H. Rabbani, and F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD,” J. Med. Signals Sens. 6(3), 166–171 (2016).
[PubMed]

Raj, A.

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
[Crossref] [PubMed]

Rauser, M. E.

D. J. Browning, A. R. Glassman, L. P. Aiello, R. W. Beck, D. M. Brown, D. S. Fong, N. M. Bressler, R. P. Danis, J. L. Kinyoun, Q. D. Nguyen, A. R. Bhavsar, J. Gottlieb, D. J. Pieramici, M. E. Rauser, R. S. Apte, J. I. Lim, P. H. Miskala, and Diabetic Retinopathy Clinical Research Network, “Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema,” Ophthalmology 114(3), 525–536 (2007).
[Crossref] [PubMed]

Sadda, S. R.

Z. Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, and S. R. Sadda, “Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images,” Invest. Ophthalmol. Vis. Sci. 54(13), 8375–8383 (2013).
[Crossref] [PubMed]

P. A. Keane and S. R. Sadda, “Predicting visual outcomes for macular disease using optical coherence tomography,” Saudi J. Ophthalmol. 25(2), 145–158 (2011).
[Crossref] [PubMed]

S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Sahni, J.

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
[Crossref] [PubMed]

J. Sahni, P. Stanga, D. Wong, and S. Harding, “Optical coherence tomography in photodynamic therapy for subfoveal choroidal neovascularisation secondary to age related macular degeneration: a cross sectional study,” Br. J. Ophthalmol. 89(3), 316–320 (2005).
[Crossref] [PubMed]

Sahni, J. N.

A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
[Crossref] [PubMed]

Saidha, S.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, S. Saidha, and J. L. Prince, Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study, Proc SPIE Int Soc Opt Eng. 9417 (2015).

Šarunic, M.

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(7), 075008 (2016).
[Crossref] [PubMed]

Schlanitz, F.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Schmidt-Erfurth, U. M.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Schriefl, S.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Schütze, C.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Simader, C.

I. Golbaz, C. Ahlers, G. Stock, C. Schütze, S. Schriefl, F. Schlanitz, C. Simader, C. Prünte, and U. M. Schmidt-Erfurth, “Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy,” Invest. Ophthalmol. Vis. Sci. 52(3), 1599–1605 (2011).
[Crossref] [PubMed]

Soubrane, G.

G. Coscas, J. Cunha-Vaz, and G. Soubrane, “Macular edema: definition and basic concepts,” Dev. Ophthalmol. 47, 1–9 (2010).
[Crossref] [PubMed]

Srinivas, S.

S. Srinivas, M. G. Nittala, A. Hariri, M. Pfau, J. Gasperini, M. Ip, and S. R. Sadda, “Quantification of intraretinal hard exudates in eyes with diabetic retinopathy by optical coherence tomography,” Retina1 (2017).

Stanga, P.

J. Sahni, P. Stanga, D. Wong, and S. Harding, “Optical coherence tomography in photodynamic therapy for subfoveal choroidal neovascularisation secondary to age related macular degeneration: a cross sectional study,” Br. J. Ophthalmol. 89(3), 316–320 (2005).
[Crossref] [PubMed]

Stangos, A. N.

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286 (2013).
[Crossref] [PubMed]

A. Kumar, J. N. Sahni, A. N. Stangos, C. Campa, and S. P. Harding, “Effectiveness of ranibizumab for neovascular age-related macular degeneration using clinician-determined retreatment strategy,” Br. J. Ophthalmol. 95(4), 530–533 (2011).
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Stock, G.

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A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Trans. Med. Imaging 13(4), 716–724 (1994).
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Figures (5)

Fig. 1
Fig. 1 Schematic of deep learning module using a total of 18 convolutional layers. CN, convolutional neural network layer. ReLU, rectified linear unit. The first number after CN refers to the number of filters and the second set of number refers to the filter sizes. The Max Pooling and Upsampling windows are also shown. Stride was set to 1 on all layers, and merge was performed using concatenation.
Fig. 2
Fig. 2 Visualization of trained weights of the first layer of convolutional filters.
Fig. 3
Fig. 3 Learning curve of the neural network training and validation.
Fig. 4
Fig. 4 Example segmentations of intraretinal fluid (IRF) from the held out test set by deep learning. A, an example optical coherence tomography (OCT) image with intraretinal fluid. B, manual segmentation of IRF C, automated segmentation by deep learning. D, an example optical coherence tomography (OCT) image with intraretinal fluid. E, manual segmentation of IRF. F, automated segmentation by deep learning. G, an example OCT image with IRF and pigment epithelial detachment (PED). H, manual segmentation of IRF avoiding PED. I, deep learning correctly segments IRF cysts but not PED. J, an example optical coherence tomography (OCT) image with IRF and shadowing under vasculature. K, manual segmentation of IRF. L, deep learning correctly segments IRF cysts but avoids the shadowing under the retinal vasculature.
Fig. 5
Fig. 5 Pairwise comparison of Dice coefficients from the held out test set. The mean Dice coefficient for human interrater reliability and deep learning were 0.750 and 0.729, respectively.

Tables (1)

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Table 1 Mean dice coefficients with standard deviations.

Equations (1)

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Dice= 2TruePositives+smooth 2TruePositives+FalsePositives+FalseNegatives+smooth

Metrics