Abstract

Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization. We present a novel methodology to identify these fluid accumulations by means of generating binary maps (offering a direct representation of these areas) and heat maps (containing the region confidence). To achieve this, a set of 312 intensity and texture-based features were studied. The most relevant features were selected using the sequential forward selection (SFS) strategy and tested with three archetypal classifiers: LDC, SVM and Parzen window. Finally, the most proficient classifier is used to create the proposed maps. All of the tested classifiers returned satisfactory results, the best classifier achieving a mean test accuracy higher than 94% in all of the experiments. The suitability of the maps was evaluated in a context of a screening issue with three different datasets obtained with two different devices, testing the capabilities of the system to work independently of the used OCT device. The experiments with the map creation were performed using 323 OCT images. Using only the binary maps, more than 91.33% of the images were correctly classified. With only the heat maps, the proposed methodology correctly separated 93.50% of the images.

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

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2018 (6)

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 Transactions on Biomed. Eng. 65, 87–95 (2018).
[Crossref]

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 125, 549–558 (2018).
[Crossref]

K. Gopinath and J. Sivaswamy, “Segmentation of retinal cysts from optical coherence tomography volumes via selective enhancement,” IEEE J. Biomed. Heal. Informatics 2018, 1 (2018).

F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9, 1545–1569 (2018).
[Crossref] [PubMed]

G. N. Girish, B. Thakur, S. Roychowdhury, A. Kothari, and J. Rajan, “Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model,” IEEE J. Biomed. Heal. Informatics 99, 1 (2018).

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

2017 (7)

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 network,” CoRR 2161, 2161 (2017).

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]

L. de Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. L. Rubin, “Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8, 1926–1949 (2017).
[Crossref] [PubMed]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “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]

A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
[Crossref]

A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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. Sahoo, S. Pal, and M. Mitra, “Automatic segmentation of accumulated fluid inside the retinal layers from optical coherence tomography images,” Measurement 101, 138–144 (2017).
[Crossref]

2016 (3)

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]

M. Esmaeili, A. 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 Sensors 6, 166–171 (2016).

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

2015 (3)

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]

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. D. Abràmoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE transactions on medical imaging 34, 1616–1623 (2015).
[Crossref]

M. Haghighata, S. Zonouzb, and M. Abdel-Mottaleba, “Cloudid: Trustworthy cloud-based and cross-enterprise biometric identification,” Expert. Syst. with Appl. 42, 7905–7916 (2015).
[Crossref]

2012 (2)

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

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 Transactions on Med. Imaging 31, 1521–1531 (2012).
[Crossref]

2010 (2)

P. Jindahra, T. R. Hedges, C. E. Mendoza-Santiesteban, and G. T. Plant, “Optical coherence tomography of the retina: applications in neurology,” Curr. Opin. Neurol. 23, 16–23 (2010).
[Crossref]

S. Chiu, X. Li, P. Nicholas, C. Toth, J. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SD-OCT images congruent with expert manual segmentation,” Opt. Express 10, 19413–19428 (2010).
[Crossref]

2008 (2)

O. Al-Kadi and D. Watson, “Texture analysis of aggressive and nonaggressive lung tumor CE CT images,” IEEE Transactions on Biomed. Eng. 55, 1822–1830 (2008).
[Crossref]

T. T. Nguyen, J. J. Wang, A. R. Sharrett, F. A. Islam, R. Klein, B. E. Klein, M. F. Cotch, and T. Y. Wong, “Relationship of retinal vascular caliber with diabetes and retinopathy,” Diabetes Care 31, 544–549 (2008).
[Crossref]

2007 (1)

E. Gordon-Lipkin, B. Chodkowski, D. Reich, S. Smith, M. Pulicken, L. Balcer, E. Frohman, G. Cutter, and P. Calabresi, “Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis,” Neurology 69, 1603–1609 (2007).
[Crossref] [PubMed]

2005 (1)

A. Pose-Reino, F. Gómez-Ulla, B. Hayik, M. Rodríguez-Fernández, M. J. Carreira-Nouche, A. Mosquera-González, M. González Penedo, and F. Gude, “Computerized measurement of retinal blood vessel calibre: description, validation and use to determine the influence of ageing and hypertension,” J. Hypertens. 23, 843–850 (2005).
[Crossref] [PubMed]

2002 (3)

T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard, “Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the atherosclerosis risk in communities study,” JAMA 287, 1153–1159 (2002).
[Crossref] [PubMed]

H. Sánchez-Tocino, A. Álvarez-Vidal, M. J. Maldonado, J. Moreno-Montañés, and A. García-Layana, “Retinal thickness study with optical coherence tomography in patients with diabetes,” Invest. Ophthal. Vis. Sci. 43, 1588–1594 (2002).
[PubMed]

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis Mach. Intell. 24, 971–987 (2002).
[Crossref]

1998 (1)

S. Buczkowski, S. Kyriacos, F. Nekka, and L. Cartilier, “The modified box-counting method: Analysis of some characteristic parameters,” Pattern Recognit. 31, 411–418 (1998).
[Crossref]

1995 (1)

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
[Crossref] [PubMed]

1976 (1)

T. Lissack and K. Fu, “Error estimation in pattern recognition via l-distance between posterior density functions,” IEEE Transactions on Inf. Theory 22, 34–45 (1976).
[Crossref]

1973 (1)

R. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” Syst. Man Cybern. IEEE Trans. on  SMC-3, 610–621 (1973).
[Crossref]

1959 (1)

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1, 269–271 (1959).
[Crossref]

1946 (1)

D. Gabor, “Theory of communication,” J. Inst. Electr. Eng. 93, 429–457 (1946).

Abdel-Mottaleba, M.

M. Haghighata, S. Zonouzb, and M. Abdel-Mottaleba, “Cloudid: Trustworthy cloud-based and cross-enterprise biometric identification,” Expert. Syst. with Appl. 42, 7905–7916 (2015).
[Crossref]

Abramoff, M. D.

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 Transactions on Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Abràmoff, M. D.

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. D. Abràmoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE transactions on medical imaging 34, 1616–1623 (2015).
[Crossref]

Al-Kadi, O.

O. Al-Kadi and D. Watson, “Texture analysis of aggressive and nonaggressive lung tumor CE CT images,” IEEE Transactions on Biomed. Eng. 55, 1822–1830 (2008).
[Crossref]

Allingham, M. J.

Álvarez-Vidal, A.

H. Sánchez-Tocino, A. Álvarez-Vidal, M. J. Maldonado, J. Moreno-Montañés, and A. García-Layana, “Retinal thickness study with optical coherence tomography in patients with diabetes,” Invest. Ophthal. Vis. Sci. 43, 1588–1594 (2002).
[PubMed]

Anima, V.

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

Baamonde, S.

S. Baamonde, J. Moura, J. Novo, and M. Ortega, “Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns,” in International Work-Conference on Artificial Neural Networks - IWANN’17, (2017), pp. 222–235.

Bab-Hadiashar, A.

R. Tennakoon, A. K. Gostar, R. Hoseinnezhad, and A. Bab-Hadiashar, “Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), (2018), pp. 1436–1440.

Balcer, L.

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G. Girish, V. Anima, A. R. Kothari, P. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography b-scans,” Comput. Methods Prog. Biomed. 153, 105–114 (2018).
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G. N. Girish, B. Thakur, S. Roychowdhury, A. Kothari, and J. Rajan, “Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model,” IEEE J. Biomed. Heal. Informatics 99, 1 (2018).

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A. González, B. Remeseiro, M. Ortega, M. Penedo, and P. Charlón, “Automatic cyst detection in OCT retinal images combining region flooding and texture analysis,” IEEE Int. Symp. on Comput. Med. Syst.397–400 (2013).

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R. Tennakoon, A. K. Gostar, R. Hoseinnezhad, and A. Bab-Hadiashar, “Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), (2018), pp. 1436–1440.

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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
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R. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” Syst. Man Cybern. IEEE Trans. on  SMC-3, 610–621 (1973).
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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 Transactions on Biomed. Eng. 65, 87–95 (2018).
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M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
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R. Tennakoon, A. K. Gostar, R. Hoseinnezhad, and A. Bab-Hadiashar, “Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), (2018), pp. 1436–1440.

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M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
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T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard, “Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the atherosclerosis risk in communities study,” JAMA 287, 1153–1159 (2002).
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Islam, F. A.

T. T. Nguyen, J. J. Wang, A. R. Sharrett, F. A. Islam, R. Klein, B. E. Klein, M. F. Cotch, and T. Y. Wong, “Relationship of retinal vascular caliber with diabetes and retinopathy,” Diabetes Care 31, 544–549 (2008).
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S. Chiu, X. Li, P. Nicholas, C. Toth, J. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SD-OCT images congruent with expert manual segmentation,” Opt. Express 10, 19413–19428 (2010).
[Crossref]

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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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M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
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Jindahra, P.

P. Jindahra, T. R. Hedges, C. E. Mendoza-Santiesteban, and G. T. Plant, “Optical coherence tomography of the retina: applications in neurology,” Curr. Opin. Neurol. 23, 16–23 (2010).
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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 network,” CoRR 2161, 2161 (2017).

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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 network,” CoRR 2161, 2161 (2017).

Klein, B. E.

T. T. Nguyen, J. J. Wang, A. R. Sharrett, F. A. Islam, R. Klein, B. E. Klein, M. F. Cotch, and T. Y. Wong, “Relationship of retinal vascular caliber with diabetes and retinopathy,” Diabetes Care 31, 544–549 (2008).
[Crossref]

T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard, “Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the atherosclerosis risk in communities study,” JAMA 287, 1153–1159 (2002).
[Crossref] [PubMed]

Klein, R.

T. T. Nguyen, J. J. Wang, A. R. Sharrett, F. A. Islam, R. Klein, B. E. Klein, M. F. Cotch, and T. Y. Wong, “Relationship of retinal vascular caliber with diabetes and retinopathy,” Diabetes Care 31, 544–549 (2008).
[Crossref]

T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard, “Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the atherosclerosis risk in communities study,” JAMA 287, 1153–1159 (2002).
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A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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]

A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
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S. Roychowdhury, D. D. Koozekanani, S. Radwan, and K. K. Parhi, “Automated localization of cysts in diabetic macular edema using optical coherence tomography images,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (IEEE, 2013), pp. 1426–1429.

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G. N. Girish, B. Thakur, S. Roychowdhury, A. Kothari, and J. Rajan, “Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model,” IEEE J. Biomed. Heal. Informatics 99, 1 (2018).

Kothari, A. R.

G. Girish, V. Anima, A. R. Kothari, P. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography b-scans,” Comput. Methods Prog. Biomed. 153, 105–114 (2018).
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G. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, (IEEE, 2016), pp. 1292–1295.

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S. Buczkowski, S. Kyriacos, F. Nekka, and L. Cartilier, “The modified box-counting method: Analysis of some characteristic parameters,” Pattern Recognit. 31, 411–418 (1998).
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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 125, 549–558 (2018).
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H. Sánchez-Tocino, A. Álvarez-Vidal, M. J. Maldonado, J. Moreno-Montañés, and A. García-Layana, “Retinal thickness study with optical coherence tomography in patients with diabetes,” Invest. Ophthal. Vis. Sci. 43, 1588–1594 (2002).
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Moreno-Montañés, J.

H. Sánchez-Tocino, A. Álvarez-Vidal, M. J. Maldonado, J. Moreno-Montañés, and A. García-Layana, “Retinal thickness study with optical coherence tomography in patients with diabetes,” Invest. Ophthal. Vis. Sci. 43, 1588–1594 (2002).
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J. Moura, J. Novo, J. Rouco, M. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid regions in optical coherence tomography,” in Conference on Artificial Intelligence in Medicine in Europe - AIME’17, (2017), pp. 305–315.

J. Moura, J. Novo, M. Ortega, and P. Charlón, “3D retinal vessel tree segmentation and reconstruction with OCT images,” in Lecture Notes in Computer Science: Image Analysis and Recognition, ICIAR’16, vol. 9730 (2016), pp. 807–816.

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J. Moura, P. L. Vidal, J. Novo, J. Rouco, and M. Ortega, “Feature definition, analysis and selection for cystoid region characterization in optical coherence tomography,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017., (2017), pp. 1369–1377.

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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 network,” CoRR 2161, 2161 (2017).

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A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
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A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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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S. Chiu, X. Li, P. Nicholas, C. Toth, J. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SD-OCT images congruent with expert manual segmentation,” Opt. Express 10, 19413–19428 (2010).
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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 Transactions on Med. Imaging 31, 1521–1531 (2012).
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J. Moura, P. L. Vidal, J. Novo, J. Rouco, and M. Ortega, “Feature definition, analysis and selection for cystoid region characterization in optical coherence tomography,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017., (2017), pp. 1369–1377.

S. Baamonde, J. Moura, J. Novo, and M. Ortega, “Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns,” in International Work-Conference on Artificial Neural Networks - IWANN’17, (2017), pp. 222–235.

J. Moura, J. Novo, M. Ortega, and P. Charlón, “3D retinal vessel tree segmentation and reconstruction with OCT images,” in Lecture Notes in Computer Science: Image Analysis and Recognition, ICIAR’16, vol. 9730 (2016), pp. 807–816.

J. Moura, J. Novo, J. Rouco, M. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid regions in optical coherence tomography,” in Conference on Artificial Intelligence in Medicine in Europe - AIME’17, (2017), pp. 305–315.

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T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis Mach. Intell. 24, 971–987 (2002).
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J. Moura, P. L. Vidal, J. Novo, J. Rouco, and M. Ortega, “Feature definition, analysis and selection for cystoid region characterization in optical coherence tomography,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017., (2017), pp. 1369–1377.

A. González, B. Remeseiro, M. Ortega, M. Penedo, and P. Charlón, “Automatic cyst detection in OCT retinal images combining region flooding and texture analysis,” IEEE Int. Symp. on Comput. Med. Syst.397–400 (2013).

J. Moura, J. Novo, M. Ortega, and P. Charlón, “3D retinal vessel tree segmentation and reconstruction with OCT images,” in Lecture Notes in Computer Science: Image Analysis and Recognition, ICIAR’16, vol. 9730 (2016), pp. 807–816.

S. Baamonde, J. Moura, J. Novo, and M. Ortega, “Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns,” in International Work-Conference on Artificial Neural Networks - IWANN’17, (2017), pp. 222–235.

J. Moura, J. Novo, J. Rouco, M. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid regions in optical coherence tomography,” in Conference on Artificial Intelligence in Medicine in Europe - AIME’17, (2017), pp. 305–315.

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M. Sahoo, S. Pal, and M. Mitra, “Automatic segmentation of accumulated fluid inside the retinal layers from optical coherence tomography images,” Measurement 101, 138–144 (2017).
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A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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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A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
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Parhi, K. K.

S. Roychowdhury, D. D. Koozekanani, S. Radwan, and K. K. Parhi, “Automated localization of cysts in diabetic macular edema using optical coherence tomography images,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (IEEE, 2013), pp. 1426–1429.

Park, H.

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 Transactions on Biomed. Eng. 65, 87–95 (2018).
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Penedo, M.

J. Moura, J. Novo, J. Rouco, M. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid regions in optical coherence tomography,” in Conference on Artificial Intelligence in Medicine in Europe - AIME’17, (2017), pp. 305–315.

A. González, B. Remeseiro, M. Ortega, M. Penedo, and P. Charlón, “Automatic cyst detection in OCT retinal images combining region flooding and texture analysis,” IEEE Int. Symp. on Comput. Med. Syst.397–400 (2013).

Philip, A.-M.

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 125, 549–558 (2018).
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T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis Mach. Intell. 24, 971–987 (2002).
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P. Jindahra, T. R. Hedges, C. E. Mendoza-Santiesteban, and G. T. Plant, “Optical coherence tomography of the retina: applications in neurology,” Curr. Opin. Neurol. 23, 16–23 (2010).
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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 125, 549–558 (2018).
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M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
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E. Gordon-Lipkin, B. Chodkowski, D. Reich, S. Smith, M. Pulicken, L. Balcer, E. Frohman, G. Cutter, and P. Calabresi, “Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis,” Neurology 69, 1603–1609 (2007).
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A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
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A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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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Radwan, S.

S. Roychowdhury, D. D. Koozekanani, S. Radwan, and K. K. Parhi, “Automated localization of cysts in diabetic macular edema using optical coherence tomography images,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (IEEE, 2013), pp. 1426–1429.

Rajan, J.

G. Girish, V. Anima, A. R. Kothari, P. Sudeep, S. Roychowdhury, and J. Rajan, “A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography b-scans,” Comput. Methods Prog. Biomed. 153, 105–114 (2018).
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G. N. Girish, B. Thakur, S. Roychowdhury, A. Kothari, and J. Rajan, “Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model,” IEEE J. Biomed. Heal. Informatics 99, 1 (2018).

G. Girish, A. R. Kothari, and J. Rajan, “Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, (IEEE, 2016), pp. 1292–1295.

Rashno, A.

A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
[Crossref]

A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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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E. Gordon-Lipkin, B. Chodkowski, D. Reich, S. Smith, M. Pulicken, L. Balcer, E. Frohman, G. Cutter, and P. Calabresi, “Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis,” Neurology 69, 1603–1609 (2007).
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Remeseiro, B.

A. González, B. Remeseiro, M. Ortega, M. Penedo, and P. Charlón, “Automatic cyst detection in OCT retinal images combining region flooding and texture analysis,” IEEE Int. Symp. on Comput. Med. Syst.397–400 (2013).

Rodríguez-Fernández, M.

A. Pose-Reino, F. Gómez-Ulla, B. Hayik, M. Rodríguez-Fernández, M. J. Carreira-Nouche, A. Mosquera-González, M. González Penedo, and F. Gude, “Computerized measurement of retinal blood vessel calibre: description, validation and use to determine the influence of ageing and hypertension,” J. Hypertens. 23, 843–850 (2005).
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J. Moura, J. Novo, J. Rouco, M. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid regions in optical coherence tomography,” in Conference on Artificial Intelligence in Medicine in Europe - AIME’17, (2017), pp. 305–315.

J. Moura, P. L. Vidal, J. Novo, J. Rouco, and M. Ortega, “Feature definition, analysis and selection for cystoid region characterization in optical coherence tomography,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017., (2017), pp. 1369–1377.

Roy, A. G.

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 network,” CoRR 2161, 2161 (2017).

Roychowdhury, S.

G. N. Girish, B. Thakur, S. Roychowdhury, A. Kothari, and J. Rajan, “Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model,” IEEE J. Biomed. Heal. Informatics 99, 1 (2018).

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

S. Roychowdhury, D. D. Koozekanani, S. Radwan, and K. K. Parhi, “Automated localization of cysts in diabetic macular edema using optical coherence tomography images,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (IEEE, 2013), pp. 1426–1429.

Rubin, D. L.

Sadeghipour, A.

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 125, 549–558 (2018).
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A. Rashno, B. Nazari, D. Koozekanani, P. Drayna, S. Sadri, H. Rabbani, and 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]

A. Rashno, D. Koozekanani, P. Drayna, B. Nazari, S. Sadri, H. Rabbani, and 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 Transactions on Biomed. Eng. 65, 989–1001 (2017).
[Crossref]

Sahoo, M.

M. Sahoo, S. Pal, and M. Mitra, “Automatic segmentation of accumulated fluid inside the retinal layers from optical coherence tomography images,” Measurement 101, 138–144 (2017).
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Sánchez-Tocino, H.

H. Sánchez-Tocino, A. Álvarez-Vidal, M. J. Maldonado, J. Moreno-Montañés, and A. García-Layana, “Retinal thickness study with optical coherence tomography in patients with diabetes,” Invest. Ophthal. Vis. Sci. 43, 1588–1594 (2002).
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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 125, 549–558 (2018).
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Schmidt-Erfurth, U.

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 125, 549–558 (2018).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
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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 network,” CoRR 2161, 2161 (2017).

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K. Gopinath and J. Sivaswamy, “Segmentation of retinal cysts from optical coherence tomography volumes via selective enhancement,” IEEE J. Biomed. Heal. Informatics 2018, 1 (2018).

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E. Gordon-Lipkin, B. Chodkowski, D. Reich, S. Smith, M. Pulicken, L. Balcer, E. Frohman, G. Cutter, and P. Calabresi, “Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis,” Neurology 69, 1603–1609 (2007).
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Sonka, M.

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. D. Abràmoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE transactions on medical imaging 34, 1616–1623 (2015).
[Crossref]

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 Transactions on Med. Imaging 31, 1521–1531 (2012).
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Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
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Sudeep, P.

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

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Swanson, E. A.

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Thakur, B.

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R. Tennakoon, A. K. Gostar, R. Hoseinnezhad, and A. Bab-Hadiashar, “Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), (2018), pp. 1436–1440.

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Figures (20)

Fig. 1
Fig. 1 OCT image portions with hardly to segment fluid areas.
Fig. 2
Fig. 2 Stages of the proposed methodology and respective steps.
Fig. 3
Fig. 3 ILM and RPE retinal layers in an OCT scan.
Fig. 4
Fig. 4 Original image and a representation of the minimum rectangular area that contains the ROI, represented as green (retinal ROI) and blue (non ROI area contained in the sampling area).
Fig. 5
Fig. 5 Binary map creation steps. With the classification results (a), we identify their original positions (b) in the OCT image and assign the surrounding pixels to their category (c).
Fig. 6
Fig. 6 Original retinal image ROI (a) and the resulting binary map (b), generated with a sample overlap of 52px.
Fig. 7
Fig. 7 Voting process steps. First, the classification results (a) are projected into the original image (b). Then, each window votes for their overlapping pixels (c). The resulting image of this voting process can be seen in (d).
Fig. 8
Fig. 8 Comparison between the grayscale normalized map and the complementary color scale proposed (heat map).
Fig. 9
Fig. 9 Final heat map, overlapped with the original OCT image. The color scale and its relationship with the resulting confidence values is also presented in the results.
Fig. 10
Fig. 10 Heat maps generated with a different sample overlap: 32px (a) and 56px (b).
Fig. 11
Fig. 11 Mean test error per feature subset of 50 iterations for the dataset trained with the Cirrus images. Vertical lines indicate the number of features that achieved the minimum mean test error value for each classifier.
Fig. 12
Fig. 12 Mean test error per feature subset of 50 iterations for the dataset trained with the Spectralis images. Vertical lines indicate the number of features that achieved the minimum mean test error value for each classifier.
Fig. 13
Fig. 13 Mean test error per feature subset of 50 iterations for the dataset trained with images coming from both capture devices. Vertical lines indicate the number of features that achieved the minimum mean test error value for each classifier.
Fig. 14
Fig. 14 Accuracy achieved using (a) the binary fluid maps and (b) the heat maps for the Cirrus image dataset. The color scale in the heat map test represents the percentage of correctly classified maps.
Fig. 15
Fig. 15 Accuracy achieved using (a) the binary fluid maps and (b) the heat maps for the Spectralis image dataset. The color scale in the heat map test represents the percentage of correctly classified maps.
Fig. 16
Fig. 16 Accuracy achieved using (a) the binary fluid maps and (b) the heat maps for the combined image dataset. The color scale in the heat map test represents the percentage of correctly classified maps.
Fig. 17
Fig. 17 Examples of true positive (green) classified samples (a) and (b), true negative (red) classified samples (c) and (d) and misclassified samples (e) for each of the considered trained models.
Fig. 18
Fig. 18 Representative map results of different complexities with images from the Spectralis capture device and the specific trained model.
Fig. 19
Fig. 19 Representative map results of different complexities with images from the Cirrus capture device and the specific trained model.
Fig. 20
Fig. 20 Representative map results of different complexities with images from both capture devices and the combined trained model.

Tables (2)

Tables Icon

Table 1 Comparative taxonomy of the state of the art. NS = not specified.

Tables Icon

Table 2 Brief descriptions of the defined feature categories.

Metrics