Abstract

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (2)

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
[Crossref]

Y. He, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls,” Data Brief 22, 601–604 (2019).
[Crossref]

2018 (3)

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 Programs Biomedicine 153, 105–114 (2018).
[Crossref]

A. Shah, L. Zhou, M. D. Abrámoff, and X. Wu, “Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in oct images,” Biomed. Opt. Express 9(9), 4509–4526 (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(4), 549–558 (2018).
[Crossref]

2017 (6)

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (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(5), 2732–2744 (2017).
[Crossref]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref]

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(7), 3440–3448 (2017).
[Crossref]

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(8), 3627–3642 (2017).
[Crossref]

Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3D deeply supervised network for automated segmentation of volumetric medical images,” Med. Image Anal. 41, 40–54 (2017).
[Crossref]

2016 (3)

S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The J. Mach. Learn. Res. 17, 1334–1373 (2016).

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

B. Knier, P. Schmidt, L. Ally, D. Buck, A. Berthele, M. Mühlau, C. Zimmer, B. Hemmer, and T. Korn, “Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis,” Brain 139(11), 2855–2863 (2016).
[Crossref]

2015 (4)

P. Bhargava, A. Lang, O. Al-Louzi, A. Carass, J. L. Prince, P. A. Calabresi, and S. Saidha, “Applying an open-source segmentation algorithm to different OCT devices in Multiple Sclerosis patients and healthy controls: implications for clinical trials,” Mult. Scler. Int. 2015, 1–10 (2015).
[Crossref]

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref]

R. S. Maldonado, P. Mettu, M. El-Dairi, and M. T. Bhatti, “The application of optical coherence tomography in neurologic diseases,” Neurol. Clin. Pract. 5(5), 460–469 (2015).
[Crossref]

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

2014 (2)

A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5(4), 1062–1074 (2014).
[Crossref]

J. González-López, G. Rebolleda, M. Leal, N. Oblanca, F. J. Muñoz-Negrete, L. Costa-Frossard, and J. C. Álvarez-Cermeño, “Comparative Diagnostic Accuracy of Ganglion Cell-Inner Plexiform and Retinal Nerve Fiber Layer Thickness Measures by Cirrus and Spectralis Optical Coherence Tomography in Relapsing-Remitting Multiple Sclerosis,” BioMed Res. Int. 2014, 1–10 (2014).
[Crossref]

2013 (2)

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref]

2012 (2)

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11(11), 963–972 (2012).
[Crossref]

J. M. Gelfand, R. Nolan, D. M. Schwartz, J. Graves, and A. J. Green, “Microcystic macular oedema in multiple sclerosis is associated with disease severity,” Brain 135(6), 1786–1793 (2012).
[Crossref]

2011 (2)

S. Saidha, S. B. Syc, M. K. Durbin, C. Eckstein, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. Newsome, J. N. Ratchford, E. M. Frohman, and P. A. Calabresi, “Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness,” Mult. Scler. 17(12), 1449–1463 (2011).
[Crossref]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134(2), 518–533 (2011).
[Crossref]

2010 (1)

2009 (2)

F. A. Medeiros, L. M. Zangwill, L. M. Alencar, C. Bowd, P. A. Sample, R. S. Jr., and R. N. Weinreb, “Detection of Glaucoma Progression with Stratus OCT Retinal Nerve Fiber Layer, Optic Nerve Head, and Macular Thickness Measurements,” Invest. Ophthalmol. Visual Sci. 50(12), 5741–5748 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[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(3), 325–332 (1995).
[Crossref]

Abbeel, P.

S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The J. Mach. Learn. Res. 17, 1334–1373 (2016).

Abramoff, M.

K. Lee, M. Abramoff, M. Garvin, and M. Sonka, “The Iowa Reference Algorithms (Retinal Image Analysis Lab, Iowa Institute for Biomedical Imaging, Iowa City, IA),” (2014).

Abrámoff, M. D.

Abràmoff, M. D.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
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Alencar, L. M.

F. A. Medeiros, L. M. Zangwill, L. M. Alencar, C. Bowd, P. A. Sample, R. S. Jr., and R. N. Weinreb, “Detection of Glaucoma Progression with Stratus OCT Retinal Nerve Fiber Layer, Optic Nerve Head, and Macular Thickness Measurements,” Invest. Ophthalmol. Visual Sci. 50(12), 5741–5748 (2009).
[Crossref]

Al-Louzi, O.

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

P. Bhargava, A. Lang, O. Al-Louzi, A. Carass, J. L. Prince, P. A. Calabresi, and S. Saidha, “Applying an open-source segmentation algorithm to different OCT devices in Multiple Sclerosis patients and healthy controls: implications for clinical trials,” Mult. Scler. Int. 2015, 1–10 (2015).
[Crossref]

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, J. L. Prince, and P. A. Calabresi, “Segmentation of microcystic macular edema in macular Cirrus data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February 21-26, 2015, vol. 9417 (2015), p. 94170P.

Ally, L.

B. Knier, P. Schmidt, L. Ally, D. Buck, A. Berthele, M. Mühlau, C. Zimmer, B. Hemmer, and T. Korn, “Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis,” Brain 139(11), 2855–2863 (2016).
[Crossref]

Álvarez-Cermeño, J. C.

J. González-López, G. Rebolleda, M. Leal, N. Oblanca, F. J. Muñoz-Negrete, L. Costa-Frossard, and J. C. Álvarez-Cermeño, “Comparative Diagnostic Accuracy of Ganglion Cell-Inner Plexiform and Retinal Nerve Fiber Layer Thickness Measures by Cirrus and Spectralis Optical Coherence Tomography in Relapsing-Remitting Multiple Sclerosis,” BioMed Res. Int. 2014, 1–10 (2014).
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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 Programs Biomedicine 153, 105–114 (2018).
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Balcer, L. J.

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
[Crossref]

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11(11), 963–972 (2012).
[Crossref]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134(2), 518–533 (2011).
[Crossref]

Beg, M. F.

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
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BenTaieb, A.

A. BenTaieb and G. Hamarneh, “Topology Aware Fully Convolutional Networks for Histology Gland Segmentation,” in 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), vol. 9901 of Lecture Notes in Computer Science (Springer Berlin Heidelberg, 2016), pp. 460–468.

Berthele, A.

B. Knier, P. Schmidt, L. Ally, D. Buck, A. Berthele, M. Mühlau, C. Zimmer, B. Hemmer, and T. Korn, “Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis,” Brain 139(11), 2855–2863 (2016).
[Crossref]

Bhargava, P.

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

P. Bhargava, A. Lang, O. Al-Louzi, A. Carass, J. L. Prince, P. A. Calabresi, and S. Saidha, “Applying an open-source segmentation algorithm to different OCT devices in Multiple Sclerosis patients and healthy controls: implications for clinical trials,” Mult. Scler. Int. 2015, 1–10 (2015).
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Bhatti, M. T.

R. S. Maldonado, P. Mettu, M. El-Dairi, and M. T. Bhatti, “The application of optical coherence tomography in neurologic diseases,” Neurol. Clin. Pract. 5(5), 460–469 (2015).
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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,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February 11 – 16, 2017, vol. 10137 (2017), p. 101371M.

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,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February 11 – 16, 2017, vol. 10137 (2017), p. 101371M.

Bogunovic, H.

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(4), 549–558 (2018).
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Bowd, C.

F. A. Medeiros, L. M. Zangwill, L. M. Alencar, C. Bowd, P. A. Sample, R. S. Jr., and R. N. Weinreb, “Detection of Glaucoma Progression with Stratus OCT Retinal Nerve Fiber Layer, Optic Nerve Head, and Macular Thickness Measurements,” Invest. Ophthalmol. Visual Sci. 50(12), 5741–5748 (2009).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2015), vol. 9351 of Lecture Notes in Computer Science (Springer Berlin Heidelberg, 2015), pp. 234–241.

Buck, D.

B. Knier, P. Schmidt, L. Ally, D. Buck, A. Berthele, M. Mühlau, C. Zimmer, B. Hemmer, and T. Korn, “Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis,” Brain 139(11), 2855–2863 (2016).
[Crossref]

Burns, T. L.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref]

Button, J.

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
[Crossref]

Cabrera Debuc, D.

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

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
[Crossref]

Y. He, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls,” Data Brief 22, 601–604 (2019).
[Crossref]

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

P. Bhargava, A. Lang, O. Al-Louzi, A. Carass, J. L. Prince, P. A. Calabresi, and S. Saidha, “Applying an open-source segmentation algorithm to different OCT devices in Multiple Sclerosis patients and healthy controls: implications for clinical trials,” Mult. Scler. Int. 2015, 1–10 (2015).
[Crossref]

A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5(4), 1062–1074 (2014).
[Crossref]

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11(11), 963–972 (2012).
[Crossref]

S. Saidha, S. B. Syc, M. K. Durbin, C. Eckstein, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. Newsome, J. N. Ratchford, E. M. Frohman, and P. A. Calabresi, “Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness,” Mult. Scler. 17(12), 1449–1463 (2011).
[Crossref]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134(2), 518–533 (2011).
[Crossref]

Y. Liu, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Multi-layer fast level set segmentation for macular oct,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, (IEEE, 2018), pp. 1445–1448.

Y. He, A. Carass, Y. Yun, C. Zhao, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Towards topological correct segmentation of macular oct from cascaded fcns,” in Fetal, Infant and Ophthalmic Medical Image Analysis, (Springer, 2017), pp. 202–209.

Y. He, A. Carass, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Topology guaranteed segmentation of the human retina from oct using convolutional neural networks,” arXiv preprint arXiv:1803.05120 (2018).

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, J. L. Prince, and P. A. Calabresi, “Segmentation of microcystic macular edema in macular Cirrus data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February 21-26, 2015, vol. 9417 (2015), p. 94170P.

E. K. Swingle, A. Lang, A. Carass, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Microcystic macular edema detection in retina OCT images,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February 15-20, 2014, vol. 9038 (International Society for Optics and Photonics, (2014), p. 90380G.

Carass, A.

Y. He, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls,” Data Brief 22, 601–604 (2019).
[Crossref]

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

P. Bhargava, A. Lang, O. Al-Louzi, A. Carass, J. L. Prince, P. A. Calabresi, and S. Saidha, “Applying an open-source segmentation algorithm to different OCT devices in Multiple Sclerosis patients and healthy controls: implications for clinical trials,” Mult. Scler. Int. 2015, 1–10 (2015).
[Crossref]

A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5(4), 1062–1074 (2014).
[Crossref]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref]

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,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February 11 – 16, 2017, vol. 10137 (2017), p. 101371M.

A. Carass and J. L. Prince, “An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging,” in Medical Image Recognition, Segmentation and Parsing, S. K. Zhou, ed. (Academic Press, 2016), pp. 259–279.

Y. Liu, A. Carass, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Multi-layer fast level set segmentation for macular oct,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, (IEEE, 2018), pp. 1445–1448.

Y. He, A. Carass, Y. Yun, C. Zhao, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Towards topological correct segmentation of macular oct from cascaded fcns,” in Fetal, Infant and Ophthalmic Medical Image Analysis, (Springer, 2017), pp. 202–209.

Y. He, A. Carass, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Topology guaranteed segmentation of the human retina from oct using convolutional neural networks,” arXiv preprint arXiv:1803.05120 (2018).

E. K. Swingle, A. Lang, A. Carass, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Microcystic macular edema detection in retina OCT images,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February 15-20, 2014, vol. 9038 (International Society for Optics and Photonics, (2014), p. 90380G.

E. K. Swingle, A. Lang, A. Carass, O. Al-Louzi, J. L. Prince, and P. A. Calabresi, “Segmentation of microcystic macular edema in macular Cirrus data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February 21-26, 2015, vol. 9417 (2015), p. 94170P.

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,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February 11 – 16, 2017, vol. 10137 (2017), p. 101371M.

S. Han, Y. He, A. Carass, S. H. Ying, and J. L. Prince, “Cerebellum parcellation with convolutional neural networks,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2019), Houstan, CA, February 16 – 21, 2019, vol. 10949 (2019), p. 109490K.

Charlier, B.

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
[Crossref]

Charon, N.

S. Lee, N. Charon, B. Charlier, K. Popuri, E. Lebed, M. V. Sarunic, A. Trouvé, and M. F. Beg, “Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework,” Med. Image Anal. 35, 570–581 (2017).
[Crossref]

Chen, H.

Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3D deeply supervised network for automated segmentation of volumetric medical images,” Med. Image Anal. 41, 40–54 (2017).
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Chiu, S. J.

Conger, A.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

S. Saidha, S. B. Syc, M. K. Durbin, C. Eckstein, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. Newsome, J. N. Ratchford, E. M. Frohman, and P. A. Calabresi, “Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness,” Mult. Scler. 17(12), 1449–1463 (2011).
[Crossref]

Conjeti, S.

Costa-Frossard, L.

J. González-López, G. Rebolleda, M. Leal, N. Oblanca, F. J. Muñoz-Negrete, L. Costa-Frossard, and J. C. Álvarez-Cermeño, “Comparative Diagnostic Accuracy of Ganglion Cell-Inner Plexiform and Retinal Nerve Fiber Layer Thickness Measures by Cirrus and Spectralis Optical Coherence Tomography in Relapsing-Remitting Multiple Sclerosis,” BioMed Res. Int. 2014, 1–10 (2014).
[Crossref]

Crainiceanu, C.

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
[Crossref]

Crainiceanu, C. M.

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11(11), 963–972 (2012).
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Cunefare, D.

Dahl, G. E.

G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving deep neural networks for lvcsr using rectified linear units and dropout,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, (IEEE, 2013), pp. 8609–8613.

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S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The J. Mach. Learn. Res. 17, 1334–1373 (2016).

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

de Boer, J. F.

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
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Deruyter, N. P.

Dou, Q.

Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3D deeply supervised network for automated segmentation of volumetric medical images,” Med. Image Anal. 41, 40–54 (2017).
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Du, D.

S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr, “Conditional random fields as recurrent neural networks,” in Proceedings of the IEEE International Conference on Computer Vision, (2015), pp. 1529–1537.

Durbin, M. K.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

S. Saidha, S. B. Syc, M. K. Durbin, C. Eckstein, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. Newsome, J. N. Ratchford, E. M. Frohman, and P. A. Calabresi, “Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness,” Mult. Scler. 17(12), 1449–1463 (2011).
[Crossref]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134(2), 518–533 (2011).
[Crossref]

Eckstein, C.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology 80(1), 47–54 (2013).
[Crossref]

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B. Knier, P. Schmidt, L. Ally, D. Buck, A. Berthele, M. Mühlau, C. Zimmer, B. Hemmer, and T. Korn, “Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis,” Brain 139(11), 2855–2863 (2016).
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Ann. Clin. Transl. Neurol. (1)

A. Rothman, O. C. Murphy, K. C. Fitzgerald, J. Button, E. Gordon-Lipkin, J. N. Ratchford, S. D. Newsome, E. M. Mowry, E. S. Sotirchos, S. B. Syc-Mazurek, J. Nguyen, N. Gonzalez Caldito, L. J. Balcer, E. M. Frohman, T. C. Frohman, D. S. Reich, C. Crainiceanu, S. Saidha, and P. A. Calabresi, “Retinal measurements predict 10-year disability in multiple sclerosis,” Ann. Clin. Transl. Neurol. 6(2), 222–232 (2019).
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Arch. Ophthalmol. (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(3), 325–332 (1995).
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Figures (11)

Fig. 1.
Fig. 1. B-scan with MME (left) and manual labels (right). The MME pseudocysts are denoted in red (right).
Fig. 2.
Fig. 2. Architecture of the proposed method. A 128$\times$128 patch extracted from a flattened B-scan is segmented by S-Net. S-Net outputs an 11 (or 10)$\times$128$\times$128 segmentation probability map. R-Net takes the S-Net outputs and generates 128$\times$9 outputs corresponding to the nine surface distances across the 128 A-scans.
Fig. 3.
Fig. 3. A schematic of our S-Net, based on U-Net [31].
Fig. 4.
Fig. 4. Shown in the top row are masks generated from ground truth surface positions before the addition of simulated defects. On the bottom row, we see the affects of the addition/subtraction of ellipses and additive Gaussian noise to the ground truth masks. The pairs of ground truth surface position and simulated masks with defects are used to train R-Net.
Fig. 5.
Fig. 5. A schematic of the patch concatenation.
Fig. 6.
Fig. 6. An example B-scan with manually delineated boundaries separating the following retinal layers: the retinal nerve fiber layer (RNFL), the ganglion cell layer (GCL); the inner plexiform layer (IPL); the inner nuclear layer (INL); the outer plexiform layer (OPL); the outer nuclear layer (ONL), the inner segment (IS); the outer segment (OS); and the retinal pigment epithelium (RPE). Boundaries (surfaces) between these layers are identified by hyphenating their acronyms. The named boundaries are: the inner limiting membrane (ILM); the external limiting membrane (ELM); and Bruch’s Membrane (BM).
Fig. 7.
Fig. 7. Shown overlaid on a B-scan are the delineations from the (a) manual delineation, (b) AURA toolkit, (c) SR-Net, (d) SR-Net-T. (e) is the S-Net result overlaid with SR-Net surface result and (f) is the intermediate S-Net result of SR-Net-T overlaid with the SR-Net-T surface result.
Fig. 8.
Fig. 8. In the left column are S-Net results showing incorrect segmentations and topology defects. The right column shows the corrected segmentation generated by SR-Net.
Fig. 9.
Fig. 9. Results of surface and MME segmentation. Shown are (a) the ground truth, (b) our proposed deep network, and (c) the MME segmentation generated by Lang et al.’s [40] random forest approach.
Fig. 10.
Fig. 10. Visualization of MME (red) and surfaces (left) and the cross-section (right).
Fig. 11.
Fig. 11. MME projection on the fundus image. Each row is one example scan. From left to right: Lang et al.’s method [40], our method, and the manual delineation.

Tables (4)

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Table 1. Dice scores for each tested method compared to manual delineation. Larger values are better and the best result in each column is denoted in bold font.

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Table 2. Mean absolute distance (MAD) and rooted mean square error (RMSE) evaluated on 20 manually delineated scans for four of the tested methods. Smaller values are better and the best value for both MAD and RMSE are shown in bold font.

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Table 3. Mean absolute distance (MAD) and rooted mean square error (RMSE) evaluated on 12 subjects (12 B-scans per subject) with our proposed SR-Net on subjects with MME.

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Table 4. Dice scores, against manual delineations, for MME cyst comparison for our proposed method and the state-of-the-art Lang et al. [40]. Higher values are better, with the best result in each column denoted in bold. We also list the total number of cyst pixels for each subject.

Equations (5)

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S i , j = { 1 , if i < B 1 , j and S M i , j = 0 10 , if i B 9 , j and S M i , j = 0 11 , if S M i , j = 1 l + 1 , if B l , j i < B l + 1 , j
L S-Net = 1 N i = 1 N ϵ + x Ω i 2 g i ( x ) p i ( x ) ϵ + x Ω i ( g i ( x ) + p i ( x ) ) .
L R-Net = 1 9 w i = 1 9 j = 1 w ( h i ( j ) r i ( j ) ) 2 .
p ( x ) = p A ( x ) × l A + p B ( x ) × l B l A + l B .
d ( j ) = d A ( j ) × l A + d B ( j ) × l B l A + l B ,

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