Abstract

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.

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

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References

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2018 (1)

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[Crossref]

2017 (3)

2016 (3)

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, 478–489 (2016).
[Crossref] [PubMed]

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
[Crossref]

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

2015 (2)

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 Analysis 26, 146–158 (2015).
[Crossref]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (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, 1062–1074 (2014).
[Crossref] [PubMed]

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN·OCT Consensus,” Ophthalmology 121, 1572–1578 (2014).
[Crossref] [PubMed]

2013 (1)

2012 (1)

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

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,” Multiple Scler. J. 17, 1449–1463 (2011).
[Crossref]

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recognit. 44, 1590–1603 (2011).
[Crossref]

2010 (6)

V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and W. Drexler, “Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express 18, 14730–14744 (2010).
[Crossref]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
[Crossref] [PubMed]

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

D. C. DeBuc and G. M. Somfai, “Early detection of retinal thickness changes in diabetes using optical coherence tomography,” Med. Sci. Monit. 16, MT15–MT21 (2010).

S. H. Kang, S. W. Hong, S. K. Im, S. H. Lee, and M. D. Ahn, “Effect of myopia on the thickness of the retinal nerve fiber layer measured by cirrus hd optical coherence tomography,” Investig. Ophthalmol. & Vis. Sci. 51, 4075–4083 (2010).
[Crossref]

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
[Crossref] [PubMed]

2009 (3)

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
[Crossref]

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

2007 (1)

B. Li and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
[Crossref]

2006 (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach,” IEEE Trans. Patt. Anal. Mach. Intell. 28, 119–134 (2006).
[Crossref]

2003 (1)

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
[Crossref] [PubMed]

1998 (1)

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Process. 7, 359–369 (1998).
[Crossref]

1995 (1)

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
[Crossref] [PubMed]

1990 (1)

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence 12, 629–639 (1990).
[Crossref]

1959 (1)

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

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26, 297–302 (1945).
[Crossref]

Abramoff, M. D.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Abràmoff, M. D.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

Acton, S. T.

B. Li and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
[Crossref]

Ahn, M. D.

S. H. Kang, S. W. Hong, S. K. Im, S. H. Lee, and M. D. Ahn, “Effect of myopia on the thickness of the retinal nerve fiber layer measured by cirrus hd optical coherence tomography,” Investig. Ophthalmol. & Vis. Sci. 51, 4075–4083 (2010).
[Crossref]

Alasil, T.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
[Crossref] [PubMed]

Alencar, L. M.

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

Al-Louzi, O.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[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,” Multiple Scler. Int. (2015).
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Antony, B. J.

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

Balcer, L. J.

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,” The Lancet Neurol. 11, 963–972 (2012).
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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
[Crossref]

Bhargava, P.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[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,” Multiple Scler. Int. (2015).
[Crossref]

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, February11, 2017, vol. 10137 (2017), p. 101371M.

Bizheva, K.

Blachley, T. S.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
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M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
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J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
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Calabresi, P.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
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Calabresi, P. A.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[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, 1062–1074 (2014).
[Crossref] [PubMed]

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, 1133–1152 (2013).
[Crossref] [PubMed]

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

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,” Multiple Scler. J. 17, 1449–1463 (2011).
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

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,” Multiple Scler. Int. (2015).
[Crossref]

A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[Crossref]

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.
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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.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

Carass, A.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[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, 1062–1074 (2014).
[Crossref] [PubMed]

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, 1133–1152 (2013).
[Crossref] [PubMed]

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,” Multiple Scler. Int. (2015).
[Crossref]

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

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.
[Crossref]

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

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.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[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, February11, 2017, vol. 10137 (2017), p. 101371M.

A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

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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,” The Lancet Neurol. 11, 963–972 (2012).
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de Boer, J. F.

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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,” Multiple Scler. J. 17, 1449–1463 (2011).
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Fanni, P.

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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
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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,” Multiple Scler. J. 17, 1449–1463 (2011).
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H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
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Garvin, M. K.

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
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B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

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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,” The Lancet Neurol. 11, 963–972 (2012).
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I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recognit. 44, 1590–1603 (2011).
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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,” The Lancet Neurol. 11, 963–972 (2012).
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Hamarneh, G.

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He, Y.

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.
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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,” The Lancet Neurol. 11, 963–972 (2012).
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S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
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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.
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A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

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Kanamori, A.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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Keane, P. A.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
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Kwark, L.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
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Kwon, Y. H.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

Lang, A.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[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, 1062–1074 (2014).
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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, 1133–1152 (2013).
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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,” Multiple Scler. Int. (2015).
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A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
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A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
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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, February11, 2017, vol. 10137 (2017), p. 101371M.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
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B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

Lee, S. H.

S. H. Kang, S. W. Hong, S. K. Im, S. H. Lee, and M. D. Ahn, “Effect of myopia on the thickness of the retinal nerve fiber layer measured by cirrus hd optical coherence tomography,” Investig. Ophthalmol. & Vis. Sci. 51, 4075–4083 (2010).
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J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
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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 Analysis 26, 146–158 (2015).
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J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

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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.

Maeda, H.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence 12, 629–639 (1990).
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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,” Multiple Scler. J. 17, 1449–1463 (2011).
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Michels, R. P.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

Miri, M. S.

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

Mishra, A.

Nakamura, M.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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Navab, N.

Negi, A.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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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,” Multiple Scler. J. 17, 1449–1463 (2011).
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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,” The Lancet Neurol. 11, 963–972 (2012).
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Nguyen, Q. D.

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,” The Lancet Neurol. 11, 963–972 (2012).
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Nicholas, P.

Novosel, J.

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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 Analysis 26, 146–158 (2015).
[Crossref]

J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

Oakley, J. D.

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,” Multiple Scler. J. 17, 1449–1463 (2011).
[Crossref]

Oh, J.

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,” The Lancet Neurol. 11, 963–972 (2012).
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Ouyang, Y.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
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Paques, M.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recognit. 44, 1590–1603 (2011).
[Crossref]

Pedut-Kloizman, T.

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
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P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence 12, 629–639 (1990).
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Považay, B.

Prince, J. L.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[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, 1062–1074 (2014).
[Crossref] [PubMed]

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, 1133–1152 (2013).
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C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Process. 7, 359–369 (1998).
[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.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

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.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[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, February11, 2017, vol. 10137 (2017), p. 101371M.

A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
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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,” Multiple Scler. Int. (2015).
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

Puliafito, C. A.

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
[Crossref] [PubMed]

Rademaker, A.

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

Rao, H. L.

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

Ratchford, J. N.

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

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,” Multiple Scler. J. 17, 1449–1463 (2011).
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S. Saidha, C. Eckstein, and J. N. Ratchford, “Optical coherence tomography as a marker of axonal damage in multiple sclerosis,” Int. J. Clin. Rev. (2010).
[Crossref]

Rosin, P. L.

Rossant, F.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recognit. 44, 1590–1603 (2011).
[Crossref]

Roy, A. G.

Russell, S. R.

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Sadda, S.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN·OCT Consensus,” Ophthalmology 121, 1572–1578 (2014).
[Crossref] [PubMed]

Sadda, S. R.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
[Crossref] [PubMed]

Saidha, S.

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

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,” Multiple Scler. J. 17, 1449–1463 (2011).
[Crossref]

S. Saidha, C. Eckstein, and J. N. Ratchford, “Optical coherence tomography as a marker of axonal damage in multiple sclerosis,” Int. J. Clin. Rev. (2010).
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

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.
[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.

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,” Multiple Scler. Int. (2015).
[Crossref]

Sample, P. A.

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

Sarunic, M.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2009), pp. 649–656.

Schlingemann, R. O.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

Schuman, J. S.

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
[Crossref] [PubMed]

Seigo, M. A.

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

Sepah, Y. J.

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,” The Lancet Neurol. 11, 963–972 (2012).
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Seya, R.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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Sheet, D.

Shi, Y.

Y. Shi and W. C. Karl, “Real-time tracking using level sets,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2 (Citeseer, 2005), pp. 34–41.

Simonett, J.

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

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, 478–489 (2016).
[Crossref] [PubMed]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
[Crossref]

Smith, B.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2009), pp. 649–656.

Solomon, S. D.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
[Crossref]

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

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.
[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.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

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, 478–489 (2016).
[Crossref] [PubMed]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
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D. C. DeBuc and G. M. Somfai, “Early detection of retinal thickness changes in diabetes using optical coherence tomography,” Med. Sci. Monit. 16, MT15–MT21 (2010).

Sonka, M.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach,” IEEE Trans. Patt. Anal. Mach. Intell. 28, 119–134 (2006).
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B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

Sotirchos, E.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
[Crossref]

Sotirchos, E. S.

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, 1133–1152 (2013).
[Crossref] [PubMed]

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,” The Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

Spaide, R. F.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN·OCT Consensus,” Ophthalmology 121, 1572–1578 (2014).
[Crossref] [PubMed]

Staurenghi, G.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN·OCT Consensus,” Ophthalmology 121, 1572–1578 (2014).
[Crossref] [PubMed]

Stem, M. S.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
[Crossref]

Swanson, E. A.

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
[Crossref] [PubMed]

Swingle, E. K.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

Syc, S. B.

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,” Multiple Scler. J. 17, 1449–1463 (2011).
[Crossref]

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, 478–489 (2016).
[Crossref] [PubMed]

Thepass, G.

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 Analysis 26, 146–158 (2015).
[Crossref]

J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

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, 478–489 (2016).
[Crossref] [PubMed]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
[Crossref]

Tick, S.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recognit. 44, 1590–1603 (2011).
[Crossref]

Toth, C. A.

Updike, J. F.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
[Crossref] [PubMed]

Van Dijk, H. W.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

van Velthoven, M. E.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

van Vliet, L. J.

J. Novosel, K. A. Vermeer, J. H. de Jong, Z. Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imag. 36, 1276–1286 (2017).
[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 Analysis 26, 146–158 (2015).
[Crossref]

J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

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, 478–489 (2016).
[Crossref] [PubMed]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. Cabrera DeBuc, “Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region,” PLoS ONE 10, 1–20 (2015).
[Crossref]

Verbraak, F. D.

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
[Crossref]

Vermeer, K. A.

J. Novosel, K. A. Vermeer, J. H. de Jong, Z. Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imag. 36, 1276–1286 (2017).
[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 Analysis 26, 146–158 (2015).
[Crossref]

J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

Volpe, N. J.

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

Wachinger, C.

Walsh, A. C.

T. Alasil, P. A. Keane, J. F. Updike, L. Dustin, Y. Ouyang, A. C. Walsh, and S. R. Sadda, “Relationship between optical coherence tomography retinal parameters and visual acuity in diabetic macular edema,” Ophthalmology 117, 2379–2386 (2010).
[Crossref] [PubMed]

Wang, C.

Wang, Z.

J. Novosel, K. A. Vermeer, J. H. de Jong, Z. Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imag. 36, 1276–1286 (2017).
[Crossref]

Ward, M.

B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

Weinreb, R. N.

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

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B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

Wong, A.

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J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
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M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
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K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach,” IEEE Trans. Patt. Anal. Mach. Intell. 28, 119–134 (2006).
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C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Process. 7, 359–369 (1998).
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A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2009), pp. 649–656.

Ying, H. S.

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, 1062–1074 (2014).
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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, 1133–1152 (2013).
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A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[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, February11, 2017, vol. 10137 (2017), p. 101371M.

Yun, Y.

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.
[Crossref]

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

Zangwill, L. M.

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
[Crossref] [PubMed]

Zhao, C.

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.
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B. Knoll, J. Simonett, N. J. Volpe, S. Farsiu, M. Ward, A. Rademaker, S. Weintraub, and A. A. Fawzi, “Retinal nerve fiber layer thickness in amnestic mild cognitive impairment: Case-control study and meta-analysis,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 4, 85–93 (2016).

Am. J. Ophthalmol. (1)

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135, 513–520 (2003).
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J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of nerve fiber layer thickness in normal and glaucomatous eyes using optical coherence tomography: a pilot study,” Arch. Ophthalmol. 113, 586–596 (1995).
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J. Novosel, K. A. Vermeer, J. H. de Jong, Z. Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imag. 36, 1276–1286 (2017).
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IEEE Trans. Patt. Anal. Mach. Intell. (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach,” IEEE Trans. Patt. Anal. Mach. Intell. 28, 119–134 (2006).
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B. Li and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
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C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Process. 7, 359–369 (1998).
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IEEE Transactions on Med. Imaging (1)

M. K. Garvin, M. D. Abramoff, 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 Transactions on Med. Imaging 28, 1436–1447 (2009).
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Investig. Ophthalmol. & Vis. Sci. (3)

H. W. Van Dijk, P. H. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. Michels, M. E. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abramoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Investig. Ophthalmol. & Vis. Sci. 50, 3404–3409 (2009).
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J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry,” Investig. Ophthalmol. & Vis. Sci. 57, 208–217 (2016).
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S. H. Kang, S. W. Hong, S. K. Im, S. H. Lee, and M. D. Ahn, “Effect of myopia on the thickness of the retinal nerve fiber layer measured by cirrus hd optical coherence tomography,” Investig. Ophthalmol. & Vis. Sci. 51, 4075–4083 (2010).
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J. Biophotonics (1)

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, 478–489 (2016).
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Med. Image Analysis (2)

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of SD-OCT data using macular flatspace,” Med. Image Analysis 43, 85–97 (2018).
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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 Analysis 26, 146–158 (2015).
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Ophthalmology (3)

H. L. Rao, L. M. Zangwill, R. N. Weinreb, P. A. Sample, L. M. Alencar, and F. A. Medeiros, “Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis,” Ophthalmology 117, 1692–1699 (2010).
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G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN·OCT Consensus,” Ophthalmology 121, 1572–1578 (2014).
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PLoS ONE (1)

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S. Saidha, C. Eckstein, and J. N. Ratchford, “Optical coherence tomography as a marker of axonal damage in multiple sclerosis,” Int. J. Clin. Rev. (2010).
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B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI2012), San Diego, CA, February4, vol. 8314, p. 83141G2012.

B. J. Antony, M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes,” in 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), vol. 8673 of Lecture Notes in Computer Science (SpringerBerlin Heidelberg, 2014), pp. 739–746.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. D. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97841C.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2009), pp. 649–656.

J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. Van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, (Citeseer, 2013), pp. 1010–1013.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February, 2013, vol. 8669 (2013), p. 86690R.
[Crossref]

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.
[Crossref]

Y. Yun, A. Carass, A. Lang, J. L. Prince, and B. J. Antony, “Collaborative SDOCT Segmentation and Analysis Software,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2017), Orlando, FL, February11, 2017, vol. 10138 (2017), p. 1013813.

A. Lang, A. Carass, P. A. Calabresi, H. S. Ying, and J. L. Prince, “An adaptive grid for graph-based segmentation in macular cube OCT,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February15, 2014, vol. 9034 (2014), p. 90340A.

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Longitudinal graph-based segmentation of macular OCT using fundus alignment,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2015), Orlando, FL, February21, 2015, vol. 9413 (2015), p. 94130M.
[Crossref]

A. Lang, A. Carass, O. Al-Louzi, P. Bhargava, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Combined registration and motion correction of longitudinal retinal OCT data,” in Proceedings of SPIE Medical Imaging (SPIE-MI 2016), San Diego, CA, February 27–March 3, 2016, vol. 9784 (2016), p. 97840X.
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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, February11, 2017, vol. 10137 (2017), p. 101371M.

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,” Multiple Scler. Int. (2015).
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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. Shi and W. C. Karl, “Real-time tracking using level sets,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2 (Citeseer, 2005), pp. 34–41.

A. Lang, A. Carass, B. M. Jedynak, S. D. Solomon, P. A. Calabresi, and J. L. Prince, “Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace,” in 13th International Symposium on Biomedical Imaging (ISBI 2016), (2016), pp. 197–200.

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Figures (10)

Fig. 1
Fig. 1 A B-scan from a healthy subject with manual delineation of nine boundaries. The boundaries from top to bottom are ILM, RNFL-GCIP, GCIP-INL, INL-ONL, OPL-ONL, ELM, IS-OS, OS-RPE, and BM.
Fig. 2
Fig. 2 A flowchart of our proposed method.
Fig. 3
Fig. 3 The outputs of our preprocessing steps. A B-scan from a healthy subject is shown (a). The ILM and BM are detected (b) and used to produce a BM flattened B-scan (c). The image in (d) shows a normalized flattened B-scan.
Fig. 4
Fig. 4 Shown are the random forest predicted boundary locations in a B-scan.
Fig. 5
Fig. 5 Representation of the ILM surface. The red line segment shows the height of the ILM (320 voxels in this case) at the 350 th A-scan of the 40 th B-scan. Therefore in the 2D array representation of the ILM surface, the value at location ( 350 , 40 ) is 320. For a 496 × 1024 × 49 OCT volume, since there are 1024 × 49 A-scans, the 2D array for the ILM surface is of size 1024 × 49. In total nine 1024 × 49 2D arrays are needed to fully represent all nine boundaries.
Fig. 6
Fig. 6 The vector field kernel used is shown in (a). A probability map of ILM (b top) is convolved with this vector field kernel to produce the external force field. A magnified view of the force field (red arrows) is overlaid on the probability map and shown in (b bottom).
Fig. 7
Fig. 7 Shown are two surfaces in front of a B-scan. We highlight two A-scans (the 250 th and the 500 th A-scans at the 21 th B-scan). The first A-scan intersects with the IPL-INL surface and INL-OPL surface at Points C and D; the second A-scan intersects with the IPL-INL surface and INL-OPL surface at Points A and B.
Fig. 8
Fig. 8 Shown are the effect of parameter selection on absolute boundary error for four boundaries in the validation set. Each sphere corresponds to an experiment on a validation set and the parameters used are indicated by the spatial coordinates of the sphere. After all 1440 experiments, the resultant absolute error were thresholded and errors that were larger than 5% of the lowest error are shown as big red spheres. The remaining results were linearly mapped to [ 0 , 1 ] with the lowest error indicated by 0 (small blue spheres) and largest errors indicated by 1 (large yellow spheres). The parameters chosen are indicated by a magenta triangle. The shaded green region indicates the combination of parameters that our method produce lower absolute boundary error than AURA v3.4 when evaluated on the test data set. The black contour indicates the intersection of this region and each slice.
Fig. 9
Fig. 9 Shown is the mean absolute boundary error (in pixels) for all nine boundaries from iteration 1 to iteration 50. Each pixel has a physical dimension of 3.9 μm axially and 5.8 μm laterally.
Fig. 10
Fig. 10 A B-scan retinal OCT (a) and a magnified region near the fovea (b) are shown, along with corresponding manual delineation (c) and (d). The B-scan is segmented using AURA v3.4 (e) and (f), and our method (LBE) (g) and (h). The LBE mask is generated by rounding the boundary location to 0.1 voxel level.

Tables (2)

Tables Icon

Table 1 Mean and standard deviations of absolute boundary error (in pixels) are shown. Each pixel correspond a physical dimension of 3.9 microns axially and 5.8 laterally. A paired Wilcoxon signed rank test was used to test the significance of any improvement between AURA v3.4 and our method (LBE). The method with the lowest mean absolute boundary error are bolded. Boundaries that are significant (α level of 0.001) are marked with a . We include the result directly from shortest path initialization (SP) to show that our method improves on the initialization. A paired Wilcoxon signed rank test (not shown in the table) between LBE and SP shows all boundaries except BM reach a p-value of 0.0001, where the p-value of BM is 0.0143 due to the large variance in the LBE output.

Tables Icon

Table 2 Mean and standard deviations of the Dice Coefficient across the eight retinal layers. A paired Wilcoxon signed rank test was used to test the significance of any improvement between AURA v3.4 and our method (LBE). The method with the highest Dice Coefficient are bolded. Boundaries that are significant (α level of 0.001) are marked with a .

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

F s ( i ) = c s ( B s ( i ) B ( i ) ) i ,
F p ( i ) = c p ( B p ( i ) B ( i ) ) i ,
F = F ext + F int
= F ext + c s ( B s B ) i + c p ( B p B ) i
C ( i , j ) = 1 P ( i , j ) + 0.01 1 .
F u n ( j , k ) = F ext ( ceil ( B n ( j , k ) ) ) + F int n ( ceil ( B n ( j , k ) )
F l n ( j , k ) = F ext ( floor ( B n ( j , k ) ) ) + F int n ( floor ( B n ( j , k ) ) .
B n + 1 ( j , k ) floor ( B n ( j , k ) ) + | F l n ( j , k ) | | F u n ( j , k ) | + | F l n ( j , k ) |

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