Abstract

Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.

© 2017 Optical Society of America

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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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  29. G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
    [Crossref]

2017 (1)

2016 (4)

S. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomedical Opt. Express 7(7), 2888–2901 (2016).
[Crossref]

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

2015 (2)

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(4), 1172–1194 (2015).
[Crossref] [PubMed]

F. Rossant, I. Bloch, I. Ghorbel, and M. Paques, “Parallel double snakes: application to the segmentation of retinal layers in 2D-oct for pathological subjects,” Pattern Recognition 48(12), 3857–3870 (2015).
[Crossref]

2014 (2)

2013 (2)

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

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(8), 907–928 (2013).
[Crossref] [PubMed]

2010 (1)

2004 (3)

N. Nassif, B. Cense, B. H. Park, S. H. Yun, T. C. Chen, B. E. Bouma, G. J. Tearney, and J. F. de Boer, “In vivo human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography,” Opt. Lett. 29(5), 480–482 (2004).
[Crossref] [PubMed]

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

2003 (1)

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

1999 (1)

M. F. Marmor, “Mechanisms of fluid accumulation in retinal edema,” Doc. Ophthalmol. 97(3–4), 239–249 (1999).
[Crossref]

1995 (1)

R. Klein and B. E. Klein, “Vision disorders in diabetes,” Diabetes in America 1, 293 (1995).

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, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Abbas, A. K.

V. Kumar, A. K. Abbas, N. Fausto, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease (Elsevier Health Sciences, 2014).

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

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(8), 907–928 (2013).
[Crossref] [PubMed]

Ahmadi, S.-A.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings of 3D Vision, (IEEE, 2016), pp. 565–571.

Ahnelt, P. K.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Allingham, M. J.

Anger, E. M.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Aretz, H.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Arshavsky, V. Y.

Aster, J. C.

V. Kumar, A. K. Abbas, N. Fausto, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease (Elsevier Health Sciences, 2014).

Bertoldo, G.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Bloch, I.

F. Rossant, I. Bloch, I. Ghorbel, and M. Paques, “Parallel double snakes: application to the segmentation of retinal layers in 2D-oct for pathological subjects,” Pattern Recognition 48(12), 3857–3870 (2015).
[Crossref]

Bouchard, J.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Bouma, B.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Bouma, B. E.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Carlier, S. G.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Cense, B.

Chakraborthi, D.

S. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomedical Opt. Express 7(7), 2888–2901 (2016).
[Crossref]

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Chartrand, G.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” Proceedings of International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (Springer, 2016), pp. 179–187.

Chatterjee, J.

S. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomedical Opt. Express 7(7), 2888–2901 (2016).
[Crossref]

Chen, L.-C.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” ArXiv Preprint:1606.00915, (2016).

Chen, T. C.

Chen, Z.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Chiu, S. J.

Conjeti, S.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Cousins, S. W.

Cowey, A.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Cunefare, D.

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

de Boer, J. F.

De Dzanet, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Debuc, D. C.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Drexler, W.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Drozdzal, M.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” Proceedings of International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (Springer, 2016), pp. 179–187.

Dufour, P. A.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Dutta, P. K.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Fang, L.

Fanni, P.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Farsiu, S.

Fausto, N.

V. Kumar, A. K. Abbas, N. Fausto, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease (Elsevier Health Sciences, 2014).

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Gauthier, D. D.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Ghorbel, I.

F. Rossant, I. Bloch, I. Ghorbel, and M. Paques, “Parallel double snakes: application to the segmentation of retinal layers in 2D-oct for pathological subjects,” Pattern Recognition 48(12), 3857–3870 (2015).
[Crossref]

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Gusson, E.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Guymer, R. H.

Halpern, E. F.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Han, B.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” Proceedings of International Conference on Computer Vision, (IEEE, 2015), pp. 1520–1528.

Hee, M. R.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Heflin, S. J.

Hermann, B.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Hobbs, T.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Hong, S.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” Proceedings of International Conference on Computer Vision, (IEEE, 2015), pp. 1520–1528.

Houissa, K.

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Houser, S.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proceedings of International Conference on Machine Learning, (2015), pp. 448–456.

Izatt, J. A.

Jung, W. G.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Kadoury, S.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” Proceedings of International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (Springer, 2016), pp. 179–187.

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(8), 907–928 (2013).
[Crossref] [PubMed]

Kao, B.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Karri, S.

S. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomedical Opt. Express 7(7), 2888–2901 (2016).
[Crossref]

Kastrati, A.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

Katouzian, A.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Kauffman, C.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Kimball, E. S.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Klein, B. E.

R. Klein and B. E. Klein, “Vision disorders in diabetes,” Diabetes in America 1, 293 (1995).

Klein, R.

R. Klein and B. E. Klein, “Vision disorders in diabetes,” Diabetes in America 1, 293 (1995).

Kokkinos, I.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” ArXiv Preprint:1606.00915, (2016).

Kong, S. X.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

König, A.

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Kowal, J.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Kumar, V.

V. Kumar, A. K. Abbas, N. Fausto, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease (Elsevier Health Sciences, 2014).

Laine, A. F.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Li, S.

Li, X. T.

Liang, Y.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

MacNeill, B.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Marmor, M. F.

M. F. Marmor, “Mechanisms of fluid accumulation in retinal edema,” Doc. Ophthalmol. 97(3–4), 239–249 (1999).
[Crossref]

Mercanti, A.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Mettu, P. S.

Milletari, F.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings of 3D Vision, (IEEE, 2016), pp. 565–571.

Morgan, J. E.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Murphy, K.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” ArXiv Preprint:1606.00915, (2016).

Nassif, N.

Navab, N.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings of 3D Vision, (IEEE, 2016), pp. 565–571.

Nelson, J. S.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Nicholas, P.

Noh, H.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” Proceedings of International Conference on Computer Vision, (IEEE, 2015), pp. 1520–1528.

Pal, C.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” Proceedings of International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (Springer, 2016), pp. 179–187.

Panozzo, G.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Papandreou, G.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” ArXiv Preprint:1606.00915, (2016).

Paques, M.

F. Rossant, I. Bloch, I. Ghorbel, and M. Paques, “Parallel double snakes: application to the segmentation of retinal layers in 2D-oct for pathological subjects,” Pattern Recognition 48(12), 3857–3870 (2015).
[Crossref]

Park, B. H.

Parolini, B.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Pignatto, S.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Pinackatt, S.

G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an oct-based classification,” Seminars in Ophthalmology, 19, (Taylor & Francis, 2004) pp. 13–20.
[Crossref]

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Rabbani, H.

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(8), 907–928 (2013).
[Crossref] [PubMed]

Rathke, F.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D oct images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Rossant, F.

F. Rossant, I. Bloch, I. Ghorbel, and M. Paques, “Parallel double snakes: application to the segmentation of retinal layers in 2D-oct for pathological subjects,” Pattern Recognition 48(12), 3857–3870 (2015).
[Crossref]

Roy, A. G.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Sakurada, B.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Sattmann, H.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Schmidt, S.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D oct images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schnörr, C.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D oct images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schroder, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Schubert, C.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Schuman, J. S.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Sheet, D.

A. G. Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks,” IEEE J. Biomedical and Health Informatics 20(2), 606–614 (2016).
[Crossref]

A. G. Roy, S. Conjeti, S. G. Carlier, K. Houissa, A. König, P. K. Dutta, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography,” In Preceedings of International Symposium on Biomedical Imaging. (IEEE, 2016), pp. 1359–1362.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

Shishkov, M.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Smiddy, W. E.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Somfai, G. M.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Sonka, M.

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(8), 907–928 (2013).
[Crossref] [PubMed]

Srinivasan, P. P.

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, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Swanson, E. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Szegedy, C.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proceedings of International Conference on Machine Learning, (2015), pp. 448–456.

Tatrai, E.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Tearney, G.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Tearney, G. J.

Tian, J.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Toth, C. A.

Tromberg, B. J.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Unterhuber, A.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semi thin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Varga, B.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. M. Somfai, W. E. Smiddy, and D. C. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophotonics 9(5), 478–489 (2016).
[Crossref] [PubMed]

Vorontsov, E.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” Proceedings of International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (Springer, 2016), pp. 179–187.

Wang, C.

Weng, W.

W. Weng, Y. Liang, E. S. Kimball, T. Hobbs, S. X. Kong, B. Sakurada, and J. Bouchard, “Decreasing incidence of type 2 diabetes mellitus in the united states, 2007–2012: Epidemiologic findings from a large us claims database,” Diabetes Res. Clin. Pract. 117, 111–118 (2016).
[Crossref] [PubMed]

Wolf-Schnurrbusch, U.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Yabushita, H.

B. Bouma, G. Tearney, H. Yabushita, M. Shishkov, C. Kauffman, D. D. Gauthier, B. MacNeill, S. Houser, H. Aretz, and E. F. Halpern, “Evaluation of intracoronary stenting by intravascular optical coherence tomography,” Heart,  89(3), 317–320 (2003).
[Crossref] [PubMed]

Yeh, A. T.

A. T. Yeh, B. Kao, W. G. Jung, Z. Chen, J. S. Nelson, and B. J. Tromberg, “Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model,” J. Biomed. Opt. 9(2), 248–253 (2004).
[Crossref] [PubMed]

Yuille, A. L.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” ArXiv Preprint:1606.00915, (2016).

Yun, S. H.

Biomed. Opt. Express (3)

Biomedical Opt. Express (1)

S. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomedical Opt. Express 7(7), 2888–2901 (2016).
[Crossref]

Diabetes in America (1)

R. Klein and B. E. Klein, “Vision disorders in diabetes,” Diabetes in America 1, 293 (1995).

Diabetes Res. Clin. Pract. (1)

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Figures (8)

Fig. 1
Fig. 1

Segmentation results of the proposed ReLayNet of OCT frames without and with fluid mass. OCT frame without fluid, its ground truth and ReLayNet segmentation are shown in (a), (b) and (c) respectively. OCT frame with fluid, its ground truth and RelayNet predictions are shown in (d), (e) and (f) respectively. The retinal layers and fluid corresponding to each colors are presented to the right.

Fig. 2
Fig. 2

Proposed fully convolutional ReLayNet architecture. The spatial resolution of the feature maps are indicated in the boxes. The underlying layer symbols are indicated to the right.

Fig. 3
Fig. 3

Illustration of pooling and unpooling procedures. The pooling stage involves saving the intermediate pooling indices, which is leveraged in the unpooling stage preserving appropriate spatial locations.

Fig. 4
Fig. 4

Illustration of the weighting scheme for different pixels of a training B-scan OCT image. A sample OCT training B-scan is shown in (a), with its ground truth labels in (b) and the corresponding weights for training as heat map in (c). The color scheme in (b) is consistent with Figure 1.

Fig. 5
Fig. 5

Overall flow of the training and testing procedure for the proposed ReLayNet. The training procedure involves slicing of the OCT B-scans as shown above. In testing phase, the whole B-scan is segmented end-to-end.

Fig. 6
Fig. 6

Layer and fluid predictions of a Test OCT B-scan near fovea with DME manifestation, shown in (a) with the expert 1 annotations in (b), expert 2 annotations in (c), ReLayNet predictions in (d) and predictions of the defined 5 comparative methods in (e–i). CM-GDP and CM-LSE doesn’t include predictions for fluid. The fovea is indicated by the yellow arrow. The region with a small fluid mass is shown by a small white box.

Fig. 7
Fig. 7

Layer predictions of Test OCT B-scan with no fluid mass, shown in (a) with the expert 1 annotations in (b), expert 2 annotations in (c), ReLayNet predictions in (d) and predictions of the defined 5 comparative methods in (e–i).

Fig. 8
Fig. 8

Illustration of ETDRS grid with 9 zones as demarcated in (a). This represents the top view for a retinal OCT volume scan. A sample cross-sectional OCT B-scan slice corresponding to the red line in the ETDRS grid is shown in (b). The different regions of the B-scan corresponding to the different zones are indicating by yellow lines in (b).

Tables (5)

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Table 1 Salient attributes of the proposed ReLayNet are depth of the architecture, loss function, skip connection and weighting scheme in loss function. The configuration of the baselines are indicated below.

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Table 2 Comparison with Comparative Methods and Expert 2 annotations. The best performance is shown by bold, the second best is shown by ⋆ and the worst shown by †.

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Table 3 Comparison with baselines. The best performance is shown by bold, the second best is shown by ⋆ and the worst shown by †.

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Table 4 Results of 8-Fold Cross Validation on 8 Patients and ensemble performance of 8 models on rest two patients.

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Table 5 Difference in retinal overall thickness (in pixels) for 9 zones in ETDRS grid across testing subjects. The best performance is shown by bold, the second best is shown by ⋆ and the worst shown by †.

Equations (8)

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𝒥 logloss = x Ω ω ( x ) g l ( x ) log ( p l ( x ) )
𝒥 dice = 1 2 x Ω p l ( x ) g l ( x ) x Ω p l 2 ( x ) + x Ω g l 2 ( x )
ω ( x ) = 1 + ω 1 I ( | l ( x ) | > 0 ) + ω 2 I ( l ( x ) = L )
𝒥 overall = λ 1 𝒥 logloss + λ 2 𝒥 dice + λ 3 W ( ) F 2
Θ * = argmin Θ : { W ( ) , b ( ) } 𝒥 overall ( Θ )
δ 𝒥 overall δ p l ( x ) = λ 1 δ 𝒥 logloss δ p l ( x ) + λ 2 δ 𝒥 dice δ p l ( x )
δ 𝒥 logloss δ p l ( x ) = x Ω ω ( x ) g l ( x ) p l ( x )
δ 𝒥 dice δ p l ( x ) = 2 g l ( x ) ( x Ω p l 2 ( x ) + x Ω g l 2 ( x ) ) 2 p l ( x ) ( x Ω p l ( x ) g l ( x ) ) ( x Ω p l 2 ( x ) + x Ω g l 2 ( x ) ) 2

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