Abstract

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

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

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2018 (2)

Z. Ji, Q. Chen, S. Niu, T. Leng, and D. L. Rubin, “Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images,” Transl. Vis. Sci. Technol. 7, 1 (2018).
[Crossref] [PubMed]

G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
[Crossref]

2017 (11)

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

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed Opt Express 8, 3627–3642 (2017).
[Crossref] [PubMed]

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
[Crossref] [PubMed]

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

A. Rashno, D. D. Koozekanani, P. M. Drayna, B. Nazari, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithms,” IEEE Transl. Biomed. Eng.,  99, 1 (2017).
[Crossref] [PubMed]

A. Rashno, B. Nazari, D. D. Koozekanani, P. M. Drayna, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain,” PLoS ONE 12, e0186949 (2017).
[Crossref] [PubMed]

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[Crossref] [PubMed]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (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]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

2016 (9)

P. Enders, P. Scholz, P. S. Muether, and S. Fauser, “Variability of disease activity in patients treated with ranibizumab for neovascular age-related macular degeneration,” Eye (Lond) 30, 1072–1076 (2016).
[Crossref]

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
[Crossref]

S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
[Crossref]

U. Schmidt-Erfurth and S. M. Waldstein, “A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration,” Prog Retin Eye Res 50, 1–24 (2016).
[Crossref]

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed Opt Express 7, 581–600 (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, 166–171 (2016).
[PubMed]

J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
[Crossref] [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, 1577–1589 (2016).
[Crossref] [PubMed]

G. N. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 1292–1295 (2016).

2015 (3)

T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Inf Process Med Imaging 24, 437–448 (2015).
[PubMed]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference 9351, 234–241 (2015).

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

2014 (2)

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
[Crossref] [PubMed]

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

2013 (7)

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
[Crossref] [PubMed]

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, 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, 277–286 (2013).
[Crossref]

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref] [PubMed]

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
[Crossref] [PubMed]

M. Pilch, K. Stieger, Y. Wenner, M. N. Preising, C. Friedburg, E. Meyer zu Bexten, and B. Lorenz, “Automated segmentation of pathological cavities in optical coherence tomography scans,” Invest. Ophthalmol. Vis. Sci. 54, 4385–4393 (2013).
[Crossref] [PubMed]

2012 (6)

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
[Crossref] [PubMed]

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

G. R. Wilkins, O. M. Houghton, and A. L. Oldenburg, “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Trans. Biomed. Eng. 59, 1109–1114 (2012).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
[Crossref] [PubMed]

2011 (2)

CATT Research Group D. F. Martin, M. G. Maguire, G. S. Ying, J. E. Grunwald, S. L. Fine, and G. J. Jaffe, “Ranibizumab and bevacizumab for neovascular age-related macular degeneration,” N. Engl. J. Med. 364, 1897–1908 (2011).
[Crossref] [PubMed]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

2010 (1)

2008 (2)

S. Farsiu, S. J. Chiu, J. Izatt, and C. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images - art. no. 68440d,” Proc. SPIE 6844, 68440D (2008).

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref] [PubMed]

2007 (1)

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans Med Imaging 26, 761–771 (2007).
[Crossref] [PubMed]

2005 (1)

D. C. Fernandez, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans Med Imaging 24, 929–945 (2005).
[Crossref] [PubMed]

Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref] [PubMed]

Akram, M. U.

S. Khalid, M. U. Akram, T. Hassan, A. Jameel, and T. Khalil, “Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD,” J. Digi.t Imaging (2017).
[Crossref] [PubMed]

Albiani, D. A.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Allingham, M. J.

Anima, V. A.

G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
[Crossref]

Bandello, F.

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
[Crossref] [PubMed]

Beg, M. F.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Birtel, J.

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
[Crossref] [PubMed]

Bittner, A. K.

A. Lang, A. Carass, A. K. Bittner, H. S. Ying, and J. L. Prince, “Improving graph-based OCT segmentation for severe pathology in Retinitis Pigmentosa patients,” Proc SPIE Int Soc Opt Eng10137 (2017).

Bizheva, K.

Bogunovic, H.

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (2017).
[Crossref] [PubMed]

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
[Crossref] [PubMed]

Boon, C. J.

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

Bourgault, S.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference 9351, 234–241 (2015).

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, 277–286 (2013).
[Crossref]

Caramoy, A.

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

Carass, A.

A. Lang, A. Carass, A. K. Bittner, H. S. Ying, and J. L. Prince, “Improving graph-based OCT segmentation for severe pathology in Retinitis Pigmentosa patients,” Proc SPIE Int Soc Opt Eng10137 (2017).

Chen, J.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

Chen, Q.

Z. Ji, Q. Chen, S. Niu, T. Leng, and D. L. Rubin, “Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images,” Transl. Vis. Sci. Technol. 7, 1 (2018).
[Crossref] [PubMed]

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
[Crossref] [PubMed]

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed Opt Express 7, 581–600 (2016).
[Crossref] [PubMed]

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
[Crossref] [PubMed]

Chen, X.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
[Crossref] [PubMed]

Chiu, S. J.

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

S. Farsiu, S. J. Chiu, J. Izatt, and C. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images - art. no. 68440d,” Proc. SPIE 6844, 68440D (2008).

Chong, N. V.

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

Chong, V.

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
[Crossref] [PubMed]

Clausi, D. A.

Conjeti, S.

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, 3627–3642 (2017).
[Crossref] [PubMed]

Cousins, S. W.

Cunefare, D.

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]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

Danis, R. P.

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
[Crossref] [PubMed]

de Sisternes, L.

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
[Crossref] [PubMed]

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed Opt Express 7, 581–600 (2016).
[Crossref] [PubMed]

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
[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, 166–171 (2016).
[PubMed]

den Hollander, A. I.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

Deruyter, N. P.

Dieleman, S.

S. Dieleman, J. Schlüter, C. Raffel, E. Olson, S. K. Sonderby, and D. Nouri, “Lasagne: First release.” (2015).

Ding, W.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Domalpally, A.

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
[Crossref] [PubMed]

Drayna, P. M.

A. Rashno, D. D. Koozekanani, P. M. Drayna, B. Nazari, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithms,” IEEE Transl. Biomed. Eng.,  99, 1 (2017).
[Crossref] [PubMed]

A. Rashno, B. Nazari, D. D. Koozekanani, P. M. Drayna, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain,” PLoS ONE 12, e0186949 (2017).
[Crossref] [PubMed]

Eldem, B.

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
[Crossref] [PubMed]

Enders, P.

P. Enders, P. Scholz, P. S. Muether, and S. Fauser, “Variability of disease activity in patients treated with ranibizumab for neovascular age-related macular degeneration,” Eye (Lond) 30, 1072–1076 (2016).
[Crossref]

Endstrasser, F.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
[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, 166–171 (2016).
[PubMed]

Fan, W.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

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, 2732–2744 (2017).
[Crossref] [PubMed]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
[Crossref] [PubMed]

Farsiu, S.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

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]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

S. Farsiu, S. J. Chiu, J. Izatt, and C. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images - art. no. 68440d,” Proc. SPIE 6844, 68440D (2008).

Fauser, S.

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
[Crossref] [PubMed]

P. Enders, P. Scholz, P. S. Muether, and S. Fauser, “Variability of disease activity in patients treated with ranibizumab for neovascular age-related macular degeneration,” Eye (Lond) 30, 1072–1076 (2016).
[Crossref]

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

Fernandez, D. C.

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans Med Imaging 26, 761–771 (2007).
[Crossref] [PubMed]

D. C. Fernandez, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans Med Imaging 24, 929–945 (2005).
[Crossref] [PubMed]

Fine, S. L.

CATT Research Group D. F. Martin, M. G. Maguire, G. S. Ying, J. E. Grunwald, S. L. Fine, and G. J. Jaffe, “Ranibizumab and bevacizumab for neovascular age-related macular degeneration,” N. Engl. J. Med. 364, 1897–1908 (2011).
[Crossref] [PubMed]

Finger, R. P.

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
[Crossref] [PubMed]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference 9351, 234–241 (2015).

Forooghian, F.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
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Fujimura, T.

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A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (2017).
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J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
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S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
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T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
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G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
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Girish, G. N.

G. N. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 1292–1295 (2016).

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S. Zheng, Y. Song, T. Leung, and I. J. Goodfellow, “Improving the robustness of deep neural networks via stability training,” CoRR abs/1604.04326 (2016).

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L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
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Grunwald, J. E.

CATT Research Group D. F. Martin, M. G. Maguire, G. S. Ying, J. E. Grunwald, S. L. Fine, and G. J. Jaffe, “Ranibizumab and bevacizumab for neovascular age-related macular degeneration,” N. Engl. J. Med. 364, 1897–1908 (2011).
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Guymer, R. H.

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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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, 166–171 (2016).
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D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
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Harding, S. P.

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, 277–286 (2013).
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Hassan, T.

S. Khalid, M. U. Akram, T. Hassan, A. Jameel, and T. Khalil, “Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD,” J. Digi.t Imaging (2017).
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He, X.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

Hernandez, M.

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, 8375–8383 (2013).
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Holz, F. G.

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
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L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
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Houghton, O. M.

G. R. Wilkins, O. M. Houghton, and A. L. Oldenburg, “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Trans. Biomed. Eng. 59, 1109–1114 (2012).
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Hoyng, C.

Hoyng, C. B.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Hu, Z.

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, 8375–8383 (2013).
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Hwang, T. S.

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, 1577–1589 (2016).
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Iwama, D.

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
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Izatt, J. A.

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
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S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
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Jaffe, G. J.

CATT Research Group D. F. Martin, M. G. Maguire, G. S. Ying, J. E. Grunwald, S. L. Fine, and G. J. Jaffe, “Ranibizumab and bevacizumab for neovascular age-related macular degeneration,” N. Engl. J. Med. 364, 1897–1908 (2011).
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Jameel, A.

S. Khalid, M. U. Akram, T. Hassan, A. Jameel, and T. Khalil, “Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD,” J. Digi.t Imaging (2017).
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Ji, Z.

Z. Ji, Q. Chen, S. Niu, T. Leng, and D. L. Rubin, “Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images,” Transl. Vis. Sci. Technol. 7, 1 (2018).
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Jia, Y.

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, 1577–1589 (2016).
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Jonna, G.

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
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Kafieh, R.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
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Kardon, R.

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
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Karri, S. P. K.

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, 3627–3642 (2017).
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Katouzian, A.

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, 3627–3642 (2017).
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Khalid, S.

S. Khalid, M. U. Akram, T. Hassan, A. Jameel, and T. Khalil, “Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD,” J. Digi.t Imaging (2017).
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Khalil, T.

S. Khalid, M. U. Akram, T. Hassan, A. Jameel, and T. Khalil, “Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD,” J. Digi.t Imaging (2017).
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Kirchhof, B.

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Kirker, A. W.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Klevering, B. J.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
[PubMed]

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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Koozekanani, D. D.

A. Rashno, D. D. Koozekanani, P. M. Drayna, B. Nazari, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithms,” IEEE Transl. Biomed. Eng.,  99, 1 (2017).
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A. Rashno, B. Nazari, D. D. Koozekanani, P. M. Drayna, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain,” PLoS ONE 12, e0186949 (2017).
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Kothari, A. K.

G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
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Kothari, A. R.

G. N. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 1292–1295 (2016).

Kuo, A. N.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
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Kutzscher, L.

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
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Lang, A.

A. Lang, A. Carass, A. K. Bittner, H. S. Ying, and J. L. Prince, “Improving graph-based OCT segmentation for severe pathology in Retinitis Pigmentosa patients,” Proc SPIE Int Soc Opt Eng10137 (2017).

Langs, G.

J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
[Crossref] [PubMed]

S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
[Crossref]

T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Inf Process Med Imaging 24, 437–448 (2015).
[PubMed]

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
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Larsen, M.

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
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Lechanteur, Y. T.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
[Crossref] [PubMed]

Lee, A. Y.

Lee, C. S.

Lee, K.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
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Lee, S.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Leitner, R.

S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
[Crossref]

Leng, T.

Z. Ji, Q. Chen, S. Niu, T. Leng, and D. L. Rubin, “Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images,” Transl. Vis. Sci. Technol. 7, 1 (2018).
[Crossref] [PubMed]

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Transl. Vis. Sci. Technol. 6, 12 (2017).
[Crossref] [PubMed]

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed Opt Express 7, 581–600 (2016).
[Crossref] [PubMed]

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
[Crossref] [PubMed]

Leung, T.

S. Zheng, Y. Song, T. Leung, and I. J. Goodfellow, “Improving the robustness of deep neural networks via stability training,” CoRR abs/1604.04326 (2016).

Li, D.

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, 1577–1589 (2016).
[Crossref] [PubMed]

Li, P.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

Li, S.

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]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
[Crossref] [PubMed]

Liakopoulos, S.

S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
[Crossref] [PubMed]

Liefers, B.

Lim, L. S.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
[Crossref] [PubMed]

Liu, L.

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, 1577–1589 (2016).
[Crossref] [PubMed]

Loewenstein, A.

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
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A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (2017).
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U. Schmidt-Erfurth and S. M. Waldstein, “A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration,” Prog Retin Eye Res 50, 1–24 (2016).
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S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
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T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Inf Process Med Imaging 24, 437–448 (2015).
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M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
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Schuster, A. K.

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
[Crossref] [PubMed]

Seddon, J. M.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
[Crossref] [PubMed]

Sheet, D.

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, 3627–3642 (2017).
[Crossref] [PubMed]

Simader, C.

J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
[Crossref] [PubMed]

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
[Crossref]

S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
[Crossref]

Smailhodzic, D.

F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
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S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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S. Zheng, Y. Song, T. Leung, and I. J. Goodfellow, “Improving the robustness of deep neural networks via stability training,” CoRR abs/1604.04326 (2016).

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R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
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M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
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U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
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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, 277–286 (2013).
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M. Pilch, K. Stieger, Y. Wenner, M. N. Preising, C. Friedburg, E. Meyer zu Bexten, and B. Lorenz, “Automated segmentation of pathological cavities in optical coherence tomography scans,” Invest. Ophthalmol. Vis. Sci. 54, 4385–4393 (2013).
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G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
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Toth, C. A.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
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S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
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Tyring, A. J.

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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
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S. Fauser, D. Smailhodzic, A. Caramoy, J. P. van de Ven, B. Kirchhof, C. B. Hoyng, B. J. Klevering, S. Liakopoulos, and A. I. den Hollander, “Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration,” Invest. Ophthalmol. Vis. Sci. 52, 5525–5528 (2011).
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F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
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van Grinsven, M. J. J. P.

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A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (2017).
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J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
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S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
[Crossref]

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
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U. Schmidt-Erfurth and S. M. Waldstein, “A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration,” Prog Retin Eye Res 50, 1–24 (2016).
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T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Inf Process Med Imaging 24, 437–448 (2015).
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T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
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Wang, C.

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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Wang, J.

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, 1577–1589 (2016).
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Warburton, J.

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
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M. Pilch, K. Stieger, Y. Wenner, M. N. Preising, C. Friedburg, E. Meyer zu Bexten, and B. Lorenz, “Automated segmentation of pathological cavities in optical coherence tomography scans,” Invest. Ophthalmol. Vis. Sci. 54, 4385–4393 (2013).
[Crossref] [PubMed]

Wilkins, G. R.

G. R. Wilkins, O. M. Houghton, and A. L. Oldenburg, “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Trans. Biomed. Eng. 59, 1109–1114 (2012).
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Wilson, D. J.

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, 1577–1589 (2016).
[Crossref] [PubMed]

Winter, K. P.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

Wintergerst, M. W. M.

M. W. M. Wintergerst, T. Schultz, J. Birtel, A. K. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. G. Holz, and R. P. Finger, “Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review,” Transl. Vis. Sci. Technol. 6, 10 (2017).
[Crossref] [PubMed]

Wong, A.

Wong, T. Y.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
[Crossref] [PubMed]

Wright, J.

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
[Crossref]

Wu, J.

J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
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Wu, M.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

Wu, X.

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, 8375–8383 (2013).
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M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
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Wu, Y.

Ying, G. S.

CATT Research Group D. F. Martin, M. G. Maguire, G. S. Ying, J. E. Grunwald, S. L. Fine, and G. J. Jaffe, “Ranibizumab and bevacizumab for neovascular age-related macular degeneration,” N. Engl. J. Med. 364, 1897–1908 (2011).
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Yoshimura, N.

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
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Young, M.

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
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Yuan, S.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

Zhang, L.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
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Zhang, M.

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, 1577–1589 (2016).
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Zheng, L.

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
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Zheng, S.

S. Zheng, Y. Song, T. Leung, and I. J. Goodfellow, “Improving the robustness of deep neural networks via stability training,” CoRR abs/1604.04326 (2016).

Zheng, Y.

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, 277–286 (2013).
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Am. J. Ophthalmol. (1)

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, 277–286 (2013).
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Biomed Opt Express (5)

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed Opt Express 7, 581–600 (2016).
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A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunovic, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed Opt Express 8, 1874–1888 (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]

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, 1577–1589 (2016).
[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, 3627–3642 (2017).
[Crossref] [PubMed]

Biomed. Opt. Express (3)

Br. J. Ophthalmol. (1)

U. Schmidt-Erfurth, V. Chong, A. Loewenstein, M. Larsen, E. Souied, R. Schlingemann, B. Eldem, J. Mones, G. Richard, and F. Bandello, “Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA),” Br. J. Ophthalmol. 98, 1144–1167 (2014).
[Crossref] [PubMed]

Comput. Methods Programs Biomed. (1)

G. M. GN Girish, V. A. Anima, A. K. Kothari, P. V. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans,” Comput. Methods Programs Biomed. 153, 105–114 (2018).
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Conf Proc IEEE Eng Med Biol Soc (1)

W. Ding, M. Young, S. Bourgault, S. Lee, D. A. Albiani, A. W. Kirker, F. Forooghian, M. V. Sarunic, A. B. Merkur, and M. F. Beg, “Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images,” Conf Proc IEEE Eng Med Biol Soc 2013, 7388–7391 (2013).
[PubMed]

Conf. Proc. IEEE Eng. Med. Biol. Soc. (1)

G. N. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 1292–1295 (2016).

Eye (Lond) (1)

P. Enders, P. Scholz, P. S. Muether, and S. Fauser, “Variability of disease activity in patients treated with ranibizumab for neovascular age-related macular degeneration,” Eye (Lond) 30, 1072–1076 (2016).
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IEEE Trans Med Imaging (2)

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D. C. Fernandez, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans Med Imaging 24, 929–945 (2005).
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IEEE Trans. Biomed. Eng. (2)

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images with Neurosensory Retinal Detachment Guided by Enface Fundus Imaging,” IEEE Trans. Biomed. Eng.,  65, 87–95 (2017).

G. R. Wilkins, O. M. Houghton, and A. L. Oldenburg, “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Trans. Biomed. Eng. 59, 1109–1114 (2012).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (4)

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36, 407–421 (2017).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32, 2034–2049 (2013).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31, 1521–1531 (2012).
[Crossref] [PubMed]

IEEE Transl. Biomed. Eng. (1)

A. Rashno, D. D. Koozekanani, P. M. Drayna, B. Nazari, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithms,” IEEE Transl. Biomed. Eng.,  99, 1 (2017).
[Crossref] [PubMed]

Inf Process Med Imaging (1)

T. Schlegl, S. M. Waldstein, W. D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks,” Inf Process Med Imaging 24, 437–448 (2015).
[PubMed]

Invest. Ophthalmol. Vis. Sci. (5)

D. Iwama, M. Hangai, S. Ooto, A. Sakamoto, H. Nakanishi, T. Fujimura, A. Domalpally, R. P. Danis, and N. Yoshimura, “Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53, 1576–1583 (2012).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

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, 8375–8383 (2013).
[Crossref] [PubMed]

M. Pilch, K. Stieger, Y. Wenner, M. N. Preising, C. Friedburg, E. Meyer zu Bexten, and B. Lorenz, “Automated segmentation of pathological cavities in optical coherence tomography scans,” Invest. Ophthalmol. Vis. Sci. 54, 4385–4393 (2013).
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J. Med. Signals Sens. (1)

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, 166–171 (2016).
[PubMed]

J. Ophthalmol. (1)

J. Wu, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, C. Simader, S. M. Waldstein, and U. M. Schmidt-Erfurth, “Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation,” J. Ophthalmol. 2016, 3898750 (2016).
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JAMA Ophthalmol (1)

S. M. Waldstein, A. M. Philip, R. Leitner, C. Simader, G. Langs, B. S. Gerendas, and U. Schmidt-Erfurth, “Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration,” JAMA Ophthalmol 134, 182–190 (2016).
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Lancet (1)

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379, 1728–1738 (2012).
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R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
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Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17, 1058–1072 (2013).
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MICCAI 2015: 18th International Conference (1)

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: 18th International Conference 9351, 234–241 (2015).

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J. P. van de Ven, D. Smailhodzic, C. J. Boon, S. Fauser, J. M. Groenewoud, N. V. Chong, C. B. Hoyng, B. J. Klevering, and A. I. den Hollander, “Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration,” Mol. Vis. 18, 2271–2278 (2012).
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F. van Asten, M. M. Rovers, Y. T. Lechanteur, D. Smailhodzic, P. S. Muether, J. Chen, A. I. den Hollander, S. Fauser, C. B. Hoyng, G. J. van der Wilt, and B. J. Klevering, “Predicting non-response to ranibizumab in patients with neovascular age-related macular degeneration,” Ophthalmic Epidemiol. 21, 347–355 (2014).
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Ophthalmology (1)

S. M. Waldstein, J. Wright, J. Warburton, P. Margaron, C. Simader, and U. Schmidt-Erfurth, “Predictive Value of Retinal Morphology for Visual Acuity Outcomes of Different Ranibizumab Treatment Regimens for Neovascular AMD,” Ophthalmology 123, 60–69 (2016).
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Opt. Express (1)

PLoS ONE (1)

A. Rashno, B. Nazari, D. D. Koozekanani, P. M. Drayna, S. Sadri, H. Rabbani, and K. K. Parhi, “Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain,” PLoS ONE 12, e0186949 (2017).
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Proc. SPIE (1)

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T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstrasser, A. Sadeghipour, A. M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning,” Ophthalmology (2017).
[Crossref] [PubMed]

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Figures (19)

Fig. 1
Fig. 1 Examples of B-scans showing the different types of retinal fluid: Subretinal fluid (SRF) is indicated in red, while intraretinal fluid (IRF) is indicated in green. Intraretinal fluid can be subdivided in intraretinal cystoid fluid (IRC) shown in (a), and diffuse non-cystic IRF shown in (b).
Fig. 2
Fig. 2 Examples of B-scans from the EUGENDA database and the corresponding manual annotations: (a) and (c) original image, (b) and (d) corresponding retina and IRC annotations. IRC annotations are indicated in green, retina annotations are indicated in red.
Fig. 3
Fig. 3 Examples of B-scans from the OPTIMA database and the corresponding manual annotations: (a,b) Example Cirrus B-scan and the corresponding IRC annotation, (c,d) Example Nidek B-scan and IRC annotation, (e,f) Example Spectralis B-scan and IRC annotation, (g,h) Example Topcon B-scan and IRC annotation. IRC annotations are indicated in green.
Fig. 4
Fig. 4 Overview of the proposed algorithm for IRC segmentation. The first FCNNs responsible for retina segmentation is visualized in green, while the second FCNN responsible for IRC segmentation is shown in red. The retina segmentation produced by the first FCNN is stacked together with the input B-scan to form a two-channel input for the IRC segmentation.
Fig. 5
Fig. 5 Example data used for training the proposed algorithm: (a) Input B-scan, (b) the derived retina segmentation (blue), (c) the corresponding IRC annotations (red), and (d) the weight map calculated from the IRC annotation and retina segmentation. The weight for background pixels (black) is set to 0, retina pixels (blue) get a weight of 1, and IRC pixels (red) get a weight between 0 and 5.
Fig. 6
Fig. 6 Schematic overview of the proposed neural network architecture consisting of a total of 27 convolutional layers, 6 max pooling operations (orange arrows) and 6 upsample operations (green arrows), providing a receptive field of 572 × 572 pixels.
Fig. 7
Fig. 7 Receptive field (RF) sizes after a max pooling operation. With every consecutive max pooling operation the RF increases in size, allowing the image to be analyzed at multiple scales. The RFs at each level for the original U-net architecture are shown in red, while the RFs for the proposed method are shown in green, i.e., By adding two additional max pooling operations the RF for the original U-net (the red squares) can be increased to include the entire image at the highest scale. Due to the anisotropic pixel size, the receptive field covers a larger area in the transversal direction.
Fig. 8
Fig. 8 Data augmentation strategy. The training dataset is synthetically increased by data augmentation. The following augmentations are successively applied: random rotation, random cropping, random mirroring and speckle noise addition.
Fig. 9
Fig. 9 Boxplots showing the distribution of (a) the dice coefficients and (b) the area segmentation errors in test set 1. Four different approaches to include prior information are compared, i.e., without using prior information (red), using the retina segmentation as an additional input (yellow), using the retina segmentation as a weight map during training (blue), and finally the proposed method where both techniques to include prior information are used together (green).
Fig. 10
Fig. 10 Boxplots showing the distribution of (a) the dice coefficients and (b) the area segmentation errors in test set 2 Four different approaches to include prior information are compared, i.e., without using prior information (red), using the retina segmentation as an additional input (yellow), using the retina segmentation as a weight map during training (blue), and finally the proposed method where both techniques to include prior information are used together (green). The performance of the two human observers is also visualizes (brown, white)
Fig. 11
Fig. 11 Boxplots showing the distribution of the area segmentation errors in the control test set. Four different approaches to include prior information are compared, i.e., without using prior information (red), using the retina segmentation as an additional input (yellow), using the retina segmentation as a weight map during training (blue), and finally the proposed method where both techniques to include prior information are used together (green).
Fig. 12
Fig. 12 Bland altman plots visualizing the volume difference on the second test set using four different approaches to include prior information, i.e., (a) by using the retina segmentation as a weight map during training; (b) by using the retina segmentation as an additional input to the system; (c) by using both methods to include prior information simultaneously; and (d) without using the retina segmentation. The performance of the two human observers is shown in (e) and (f), respectively.
Fig. 13
Fig. 13 Example segmentation results of the proposed algorithm applied to B-scans from the second test set acquired with a Spectralis device: (a, e) Original input image, (b, f) segmentation output from the proposed algorithm (green), (c, g) manual annotations performed by human observer 1 in orange and (d, h) manual annotations performed by human observer 2 in red.
Fig. 14
Fig. 14 Example segmentation results of the proposed algorithm applied to a B-scan acquired with a Cirrus device. (a) Original input image, (b) segmentation output from the proposed algorithm in green, (c) manual annotations performed by human observer 1 in orange, (d) manual annotations performed by human observer 2 in red.
Fig. 15
Fig. 15 Example segmentation results of the proposed algorithm applied to a B-scan acquired with a Nidek device. (a) Original input image, (b) segmentation output from the proposed algorithm in green, (c) manual annotations performed by human observer 1 in orange, (d) manual annotations performed by human observer 2 in red.
Fig. 16
Fig. 16 Example segmentation results of the proposed algorithm applied to a B-scan acquired with a Spectralis device. (a) Original input image, (b) segmentation output from the proposed algorithm in green, (c) manual annotations performed by human observer 1 in orange, (d) manual annotations performed by human observer 2 in red.
Fig. 17
Fig. 17 Example segmentation results of the proposed algorithm applied to a B-scan acquired with a Topcon device. (a) Original input image, (b) segmentation output from the proposed algorithm in green, (c) manual annotations performed by human observer 1 on orange, (d) manual annotations performed by human observer 2 in red.
Fig. 18
Fig. 18 Example case where the proposed method segmented an outer retinal tubulation, a retinal pathology with a strong similarity to IRC, resulting in a dice coefficient of zero. (a) Ground truth image, (b) segmentation output by the proposed algorithm.
Fig. 19
Fig. 19 Example case where the proposed method missed an annotated cyst due to very poor image contrast in a Cirrus images from the external dataset. (a) Original input image, (b) segmentation output from the proposed algorithm (no IRC detected), (c) ground truth annotations performed by human observer 1 in orange, (d) ground truth annotations performed by human observer 2 in red.

Tables (5)

Tables Icon

Table 1 Dice coefficients, i.e., mean ± standard deviation (median) and area segmentation error obtained on the first test set using four different approaches to include prior information, i.e., 1) by using the retina segmentation as a weight map during training; 2) using the retina segmentation as an additional input to the system ; 3) both methods to include prior information used simultaneously; and 4) without using the retina segmentation.

Tables Icon

Table 2 Dice coefficients, i.e., mean ± standard deviation (median) and area segmentation error obtained on the second test set using four different approaches to include prior information, i.e., 1) by using the retina segmentation as a weight map during training; 2) using the retina segmentation as an additional input to the system ; 3) both methods to include prior information used simultaneously; and 4) without using the retina segmentation.

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Table 3 Area segmentation error obtained on the control test set using four different approaches to include prior information, i.e., 1) by using the retina segmentation as a weight map during training; 2) using the retina segmentation as an additional input to the system ; 3) both methods to include prior information used simultaneously; and 4) without using the retina segmentation.

Tables Icon

Table 4 Absolute volume difference, intraclass correlation coefficient (ICC) an Pearson’s correlation coefficient (ρ) on the second test set using four different approaches to include prior information, i.e., 1) by using the retina segmentation as a weight map during training; 2) by using the retina segmentation as an additional input to the system; 3) by using both methods to include prior information simultaneously; and 4) without using the retina segmentation. The performance of the two human observers is also included.

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Table 5 Dice coefficients, i.e., mean ± standard deviation (median), of the proposed method compared against reference standard 1 and reference standard 2 as defined in section 2.3.3

Equations (1)

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w i ( x ) = { min ( 5 , N retina N IRC ) if S i ( x ) = 1 , i . e . , x IRC 1 if S i ( x ) = 0 and R i ( x ) = 1 , i . e . , x retina 0 if S i ( x ) = 0 and R i ( x ) = 0 , i . e . , x other areas

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