Abstract

Optical coherence tomography is routinely used clinically for the detection and management of ocular diseases as well as in research where the studies may involve animals. This routine use requires that the developed automated segmentation methods not only be accurate and reliable, but also be adaptable to meet new requirements. We have previously proposed the use of a graph-theoretic approach for the automated 3-D segmentation of multiple retinal surfaces in volumetric human SD-OCT scans. The method ensures the global optimality of the set of surfaces with respect to a cost function. Cost functions have thus far been typically designed by hand by domain experts. This difficult and time-consuming task significantly impacts the adaptability of these methods to new models. Here, we describe a framework for the automated machine-learning based design of the cost function utilized by this graph-theoretic method. The impact of the learned components on the final segmentation accuracy are statistically assessed in order to tailor the method to specific applications. This adaptability is demonstrated by utilizing the method to segment seven, ten and five retinal surfaces from SD-OCT scans obtained from humans, mice and canines, respectively. The overall unsigned border position errors observed when using the recommended configuration of the graph-theoretic method was 6.45 ± 1.87 μm, 3.35 ± 0.62 μm and 9.75 ± 3.18 μm for the human, mouse and canine set of images, respectively.

© 2013 OSA

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  2. G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
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  4. T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
    [CrossRef] [PubMed]
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  6. M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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  9. M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
    [CrossRef] [PubMed]
  10. R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
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  11. A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
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    [CrossRef]
  22. A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (2013).
    [CrossRef]
  23. 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 Proc. SPIE Medical Imaging8314, 83141G (2012).
    [CrossRef]
  24. K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images–a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell.28, 119–134 (2006).
    [CrossRef] [PubMed]
  25. M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
    [CrossRef]
  26. M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
    [CrossRef] [PubMed]
  27. P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
    [CrossRef]
  28. S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
    [CrossRef]
  29. G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
    [CrossRef]
  30. W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell.13, 891–906 (1991).
    [CrossRef]
  31. L. Breiman, “Random forests,” Machine learning45, 5–32 (2001).
    [CrossRef]
  32. B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
    [CrossRef]
  33. B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).
  34. N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
    [CrossRef] [PubMed]

2013

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (2013).
[CrossRef]

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

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

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. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

2012

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[CrossRef]

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
[CrossRef] [PubMed]

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

2011

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag.30, 484–496 (2011).
[CrossRef]

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
[CrossRef] [PubMed]

K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express2, 1743–1756 (2011).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
[CrossRef]

2010

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

V. Kajić, B. Povazay, 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. Express18, 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. Express18, 19413–19428 (2010).
[CrossRef] [PubMed]

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

2009

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

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. Imag.28, 1436–1447 (2009).
[CrossRef]

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

T. Fabritius, S. Makita, M. Miura, R. Myllylä, and Y. Yasuno, “Automated segmentation of the macula by optical coherence tomography.” Opt. Express17, 15659–15669 (2009).
[CrossRef] [PubMed]

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express17, 23719–23728 (2009).
[CrossRef]

2008

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

2007

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
[CrossRef]

2006

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images–a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell.28, 119–134 (2006).
[CrossRef] [PubMed]

2005

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

2004

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
[CrossRef]

2002

S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
[CrossRef]

2001

L. Breiman, “Random forests,” Machine learning45, 5–32 (2001).
[CrossRef]

1991

W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell.13, 891–906 (1991).
[CrossRef]

Abdillahi, H.

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

Abràmoff, M. D.

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[CrossRef]

G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
[CrossRef]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[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. Imag.28, 1436–1447 (2009).
[CrossRef]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Adelson, E.

W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell.13, 891–906 (1991).
[CrossRef]

Alward, W. L. M.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

Ansari, R.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Antony, B. J.

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[CrossRef]

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

Bagci, A. M.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Bailey, T. J.

T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
[CrossRef] [PubMed]

Beck, S. C.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Bilonick, R. A.

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

Bizheva, K.

Blair, M.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Blair, N. P.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Bloch, I.

F. Rossant, I. Ghorbel, I. Bloch, M. Paques, and S. Tick, “Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures,” in Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2009) pp. 1370–1373.
[CrossRef]

Bowd, C.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

Breiman, L.

L. Breiman, “Random forests,” Machine learning45, 5–32 (2001).
[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. Imag.28, 1436–1447 (2009).
[CrossRef]

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 Proc. SPIE Medical Imaging8669, 86690R (2013).
[CrossRef]

Calabresi, P. A.

Carass, A.

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. Express4, 1133–1152 (2013).
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A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (2013).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imag.32, 531–543 (2013).
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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. Pattern Anal. Mach. Intell.28, 119–134 (2006).
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Choi, S. S. S.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
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O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
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Clausi, D. A.

Davin, S. P.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
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De Dzanet, S.

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

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imag.32, 531–543 (2013).
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M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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Farsiu, S.

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M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
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G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
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M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell.13, 891–906 (1991).
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R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
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M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
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M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
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B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
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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. Imag.28, 1436–1447 (2009).
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M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
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S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
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G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
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E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
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B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
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Hamann, B.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
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E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
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Hangai, M.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
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N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
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Hauser, M.

Hermann, B.

Hernandez-Merino, E.

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
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Hofer, B.

Hu, Z.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
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Huang, D.

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

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G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Humphries, P.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Hyde, D. R.

T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
[CrossRef] [PubMed]

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Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Ishikawa, H.

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Izatt, J. A.

Jacobson, S. J.

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
[CrossRef] [PubMed]

Jeong, W.

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

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P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
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M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

Kajic, V.

Kakizuka, A.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Kecova, H.

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
[CrossRef] [PubMed]

Kim, C. Y.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

Kim, J.

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

Kohda, H.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

Kondo, M.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

Kowal, J.

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

Kruizinga, P.

S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
[CrossRef]

Kwon, Y.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[CrossRef]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

Lang, A.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (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. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

Lee, K.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

Lemij, H. G.

Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images–a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell.28, 119–134 (2006).
[CrossRef] [PubMed]

Li, X. T.

Loog, M.

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Lu, A. T.-H.

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Magee, S. S.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

Makita, S.

Maricle, A.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
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Marshall, D.

Medeiros, F.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
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M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

Mishra, A.

Miura, M.

Muraoka, Y.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Myllylä, R.

Nakano, N.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Nicholas, P.

Niemeijer, M.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Nzokwe, R. N.

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
[CrossRef] [PubMed]

Okamoto-Furuta, K.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

Paques, M.

F. Rossant, I. Ghorbel, I. Bloch, M. Paques, and S. Tick, “Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures,” in Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2009) pp. 1370–1373.
[CrossRef]

Paquet-Durand, F.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Pennesi, M. E.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

Petkov, N.

S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
[CrossRef]

Povazay, B.

Prince, J. L.

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. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (2013).
[CrossRef]

Quellec, G.

G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
[CrossRef]

Rathke, F.

F. Rathke, S. Schmidt, and C. Schnörr, “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2011) vol. 14 pp. 370–377.

Redmond, T. M.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Remo, S.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

Rosin, P. L.

Rossant, F.

F. Rossant, I. Ghorbel, I. Bloch, M. Paques, and S. Tick, “Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures,” in Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2009) pp. 1370–1373.
[CrossRef]

Russell, S. R.

G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
[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. Imag.28, 1436–1447 (2009).
[CrossRef]

Sahaboglu-Tekgoz, A.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Sarunic, M. V.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag.30, 484–496 (2011).
[CrossRef]

Schmidt, S.

F. Rathke, S. Schmidt, and C. Schnörr, “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2011) vol. 14 pp. 370–377.

Schnörr, C.

F. Rathke, S. Schmidt, and C. Schnörr, “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2011) vol. 14 pp. 370–377.

Schröder, S.

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

Schuman, J. S.

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Seeliger, M. W.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Shahidi, M.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Shuba, L.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

Smith, B. R.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag.30, 484–496 (2011).
[CrossRef]

Sohn, E. H.

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

Sonka, M.

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[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. Imag.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. Pattern Anal. Mach. Intell.28, 119–134 (2006).
[CrossRef] [PubMed]

Sonkova, P.

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

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 Proc. SPIE Medical Imaging8669, 86690R (2013).
[CrossRef]

Sotirchos, E. S.

Staal, J. J.

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Tan, O.

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Terasaki, H.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

Tick, S.

F. Rossant, I. Ghorbel, I. Bloch, M. Paques, and S. Tick, “Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures,” in Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2009) pp. 1370–1373.
[CrossRef]

Toda, Y.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Toth, C. A.

Tu, D. C.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

van der Schoot, J.

van Ginneken, B.

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Vance, J. E.

T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
[CrossRef] [PubMed]

Varma, R.

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Vermeer, K. A.

Vessani, R.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

Viola, P.

P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
[CrossRef]

Way, O. M.

P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
[CrossRef]

Weinreb, R. N.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

Wen, Y.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

Wenzel, A.

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

Werner, J. S. S.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
[CrossRef]

Wiley, D. F. F.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
[CrossRef]

Wolf-Schnurrbusch, U.

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

Wollstein, G.

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Wong, A.

Wu, X.

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. Imag.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. Pattern Anal. Mach. Intell.28, 119–134 (2006).
[CrossRef] [PubMed]

Yasuno, Y.

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag.30, 484–496 (2011).
[CrossRef]

Ying, H. S.

Yoshimura, N.

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

Zangwill, L.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

Zawadzki, R. J.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
[CrossRef]

Zelkha, R.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Am. J. Ophthalmol.

F. Medeiros, L. Zangwill, C. Bowd, R. Vessani, S. Remo, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol.139, 44–55 (2005).
[CrossRef] [PubMed]

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol.146, 679–687 (2008).
[CrossRef] [PubMed]

Biomed. Opt. Express

IEEE Trans. Image Proc.

S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Proc.11, 1160–1167 (2002).
[CrossRef]

IEEE Trans. Med. Imag.

G. Quellec, S. R. Russell, and M. D. Abràmoff, “Optimal filter framework for automated, instantaneous detection of lesions in retinal images,” IEEE Trans. Med. Imag.30, 523–533 (2011).
[CrossRef]

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag.30, 484–496 (2011).
[CrossRef]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imag.32, 531–543 (2013).
[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. Imag.28, 1436–1447 (2009).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images–a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell.28, 119–134 (2006).
[CrossRef] [PubMed]

W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell.13, 891–906 (1991).
[CrossRef]

Int. J. Computer Vision

P. Viola, O. M. Way, and M. J. Jones, “Robust real-time face detection,” Int. J. Computer Vision57, 137–154 (2004).
[CrossRef]

Invest Ophthalmol Vis Sci

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,” Invest Ophthalmol Vis Sci48, 1665–1673 (2007).
[CrossRef] [PubMed]

Invest. Ophthalmol. Vis. Sci.

M. E. Pennesi, K. V. Michaels, S. S. Magee, A. Maricle, S. P. Davin, A. K. Garg, M. J. Gale, D. C. Tu, Y. Wen, L. R. Erker, and P. J. Francis, “Long-term characterization of retinal degeneration in rd1 and rd10 mice using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.53, 4644–4656 (2012).
[CrossRef] [PubMed]

N. Nakano, H. O. Ikeda, M. Hangai, Y. Muraoka, Y. Toda, A. Kakizuka, and N. Yoshimura, “Longitudinal and simultaneous imaging of retinal ganglion cells and inner retinal layers in a mouse model of glaucoma induced by N-methyl-D-aspartate,” Invest. Ophthalmol. Vis. Sci.52, 8754–62 (2011).
[CrossRef] [PubMed]

M. L. Gabriele, H. Ishikawa, J. S. Schuman, R. A. Bilonick, J. Kim, L. Kagemann, and G. Wollstein, “Reproducibility of spectral-domain optical coherence tomography total retinal thickness measurements in mice,” Invest. Ophthalmol. Vis. Sci.51, 6519–6523 (2010).
[CrossRef] [PubMed]

G. Huber, S. C. Beck, C. Grimm, A. Sahaboglu-Tekgoz, F. Paquet-Durand, A. Wenzel, P. Humphries, T. M. Redmond, M. W. Seeliger, and M. D. Fischer, “Spectral domain optical coherence tomography in mouse models of retinal degeneration,” Invest. Ophthalmol. Vis. Sci.50, 5888–5895 (2009).
[CrossRef] [PubMed]

T. J. Bailey, D. H. Davis, J. E. Vance, and D. R. Hyde, “Spectral-domain optical coherence tomography as a noninvasive method to assess damaged and regenerating adult zebrafish retinas,” Invest. Ophthalmol. Vis. Sci.53, 3126–3138 (2012).
[CrossRef] [PubMed]

Invest. Ophthalmol. Vis. Sci. E-Abstract 4892

B. J. Antony, M. D. Abràmoff, W. Jeong, E. H. Sohn, and M. K. Garvin, “Segmentation of multiple intra-retinal surfaces in volumetric SD-OCT images of mouse eyes using an improved Iowa reference algorithm,” Invest. Ophthalmol. Vis. Sci. E-Abstract 4892 (May2013).

J. Biomed. Opt.

R. J. Zawadzki, A. R. R. Fuller, D. F. F. Wiley, B. Hamann, S. S. S. Choi, and J. S. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt.12, 41206 (2007).
[CrossRef]

Machine learning

L. Breiman, “Random forests,” Machine learning45, 5–32 (2001).
[CrossRef]

Ophthalmology

O. Tan, V. Chopra, A. T.-H. Lu, J. S. Schuman, H. Ishikawa, G. Wollstein, R. Varma, and D. Huang, “Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography,” Ophthalmology116, 2305–2314 (2009).
[CrossRef] [PubMed]

Opt. Express

PloS One

Y. Muraoka, H. O. Ikeda, N. Nakano, M. Hangai, Y. Toda, K. Okamoto-Furuta, H. Kohda, M. Kondo, H. Terasaki, A. Kakizuka, and N. Yoshimura, “Real-time imaging of rabbit retina with retinal degeneration by using spectral-domain optical coherence tomography,” PloS One7, e36135 (2012).
[CrossRef] [PubMed]

Proc. of SPIE Medical Imaging

B. J. Antony, M. D. Abràmoff, K. Lee, P. Sonkova, P. Gupta, Y. Kwon, M. Niemeijer, Z. Hu, and M. K. Garvin, “Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images,” Proc. of SPIE Medical Imaging7626, 76260U (2010).
[CrossRef]

M. Niemeijer, J. J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. of SPIE Medical Imaging5370, 648–656 (2004).
[CrossRef]

Proc. SPIE Medical Imaging

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in Proc. SPIE Medical Imaging8669, 86690R (2013).
[CrossRef]

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 Proc. SPIE Medical Imaging8314, 83141G (2012).
[CrossRef]

Vet. Ophthalmol.

E. Hernandez-Merino, H. Kecova, S. J. Jacobson, K. N. Hamouche, R. N. Nzokwe, and S. D. Grozdanic, “Spectral domain optical coherence tomography (SD-OCT) assessment of the healthy female canine retina and optic nerve,” Vet. Ophthalmol.14, 400–405 (2011).
[CrossRef] [PubMed]

Other

F. Rossant, I. Ghorbel, I. Bloch, M. Paques, and S. Tick, “Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures,” in Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2009) pp. 1370–1373.
[CrossRef]

F. Rathke, S. Schmidt, and C. Schnörr, “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2011) vol. 14 pp. 370–377.

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Figures (9)

Fig. 1
Fig. 1

(a) Illustration of a 3-D volumetric SD-OCT scan of a human eye centered on the optic nerve head. A central slice from a volumetric scan obtained from (b) a human subject showing 7 retinal surfaces, (c) a mouse showing 10 retinal surfaces and (d) a canine showing 5 retinal surfaces.

Fig. 2
Fig. 2

(a) A slice from a mouse SD-OCT image showing the (b) five inner layers, namely the NF+GCL, the IPL, the INL, the OPL and the ONL and their categorization into high-, medium- and low-intensity regions.

Fig. 3
Fig. 3

Overview of the machine-learning-based cost function design.

Fig. 4
Fig. 4

The top row shows slices from SD-OCT volumes obtained from (left) a human, (middle) a mouse and (right) a canine. The probability maps obtained for the dark-to-bright surfaces and bright-to-dark are shown in the second and third rows, respectively. The probability maps obtained for the high-, medium and low-intensity regions are as shown in the fourth, fifth and sixth rows, respectively.

Fig. 5
Fig. 5

(a) Illustration of a volumetric SD-OCT scan obtained from a mouse, showing the circular “valid” region of the projection image. The circular nature of the scan results in large noisy regions in slices near the (b) the periphery. The optic nerve head causes shadows in the (c) central slices of the scan. (d) The evaluation of the segmentation accuracy was therefore, limited to an annular region defined by d1=0.2mm and d2=1.2mm in order to avoid the ONH and the noisy peripheral regions.

Fig. 6
Fig. 6

Regions of low signal strength and the optic nerve head region were excluded from the validation. (a) A slice from an SD-OCT scan obtained from a canine, showing the optic nerve head region within which surfaces are difficult to discern and the (b) corresponding mask. The region within the red lines was excluded.

Fig. 7
Fig. 7

(a) A near-central slice from an SD-OCT scan acquired from a human subject that presented with glaucoma alongside (b) the manual tracings obtained from an independent observer and the automated results obtained using (c) the previously described method [32] that did not incorporate any learned cost terms, (d) learned in-region cost function, (e) on-surface cost function and (f) a combination of both.

Fig. 8
Fig. 8

A near-central slice from an SD-OCT volume of a mouse retina alongside (b) the manual tracings obtained from a retinal specialist and the automated results obtained using (c) an approach that did not incorporate any learned cost terms and after the inclusion of learned (d) in-region cost function, (e) on-surface cost function and (f) a combination of both.

Fig. 9
Fig. 9

(a) A slice from an SD-OCT image obtained from a canine alongside (b) the manual tracings obtained from an independent observer and the automated results obtained using (c) the learned in-region cost term, (d) on-surface cost function, and (e) a combination of both.

Tables (3)

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Table 1 Unsigned border position error (mean ± SD) in μm computed on the human dataset.

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Table 2 Unsigned border position error (mean ± SD) in μm obtained on the mice data.

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Table 3 Overall unsigned border position error (mean ± SD) in microns obtained on the 19 canine scans.

Equations (8)

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C T = α i = 1 n C S i + ( 1 α ) j = 0 n C R j , where
C S i = ( x , y , z ) | z = S i ( x , y ) c surf i ( x , y , z ) and C R j = ( x , y , z ) R j c reg j ( x , y , z ) ,
g ( x , y ) ( σ , θ , ψ ) = e x 2 + γ 2 y 2 2 σ 2 e i ( 2 π x λ + ψ ) , where x = x cos θ + y sin θ , y = x sin θ + y cos θ .
E σ , θ ( x , y ) = G ( x , y ) ( σ , θ , 0 ° ) 2 + G ( x , y ) ( σ , θ , 90 ° ) 2 where ,
G ( x , y ) ( σ , θ , ψ ) = η = 0 N ν = 0 M I ( η , ν ) g ( σ , θ , ψ ) ( x η , y ν ) .
G 1 θ = cos ( θ ) G 1 0 + sin ( θ ) G 1 90 , where G 1 0 = x ( e ( x 2 + z 2 ) ) and G 1 90 = z ( e ( x 2 + z 2 ) ) .
G 1 0 = x ( e ( x 2 + y 2 + z 2 ) ) and G 1 90 = z ( e ( x 2 + y 2 + z 2 ) ) .
C T = i = 1 n β i C S i + j = 0 n C R j , and β i = { α i ( 1 α i ) , if 1 α i > 0 C large , if 1 α i 0 .

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