Abstract

Bruch’s membrane opening (BMO) is an important biomarker in the progression of glaucoma. Bruch’s membrane opening minimum rim width (BMO-MRW), cup-to-disc ratio in spectral domain optical coherence tomography (SD-OCT) and lamina cribrosa depth based on BMO are important measurable parameters for glaucoma diagnosis. The accuracy of measuring these parameters is significantly affected by BMO detection. In this paper, we propose a method for automatically detecting BMO in SD-OCT volumes accurately to reduce the impact of the border tissue and vessel shadows. The method includes three stages: a coarse detection stage composed by retinal pigment epithelium layer segmentation, optic disc segmentation, and multi-modal registration; a fixed detection stage based on the U-net in which BMO detection is transformed into a region segmentation problem and an area bias component is proposed in the loss function; and a post-processing stage based on the consistency of results to remove outliers. Experimental results show that the proposed method outperforms previous methods and achieves a mean error of 42.38 μm.

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

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References

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  1. R. N. Weinreb and P. T. Khaw, “Primary open-angle glaucoma,” The Lancet 363, 1711–1720 (2004).
    [Crossref]
  2. S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
    [Crossref] [PubMed]
  3. J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
    [Crossref]
  4. A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
    [Crossref] [PubMed]
  5. A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
    [Crossref]
  6. B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
    [Crossref]
  7. J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
    [Crossref]
  8. Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
    [Crossref]
  9. Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
    [Crossref] [PubMed]
  10. F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.
  11. J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
    [Crossref] [PubMed]
  12. Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.
  13. H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).
  14. Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
    [Crossref]
  15. M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
    [Crossref] [PubMed]
  16. H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
    [Crossref]
  17. A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.
  18. M. A. Hussain, A. Bhuiyan, and K. Ramamohanarao, “Disc segmentation and bmo-mrw measurement from sd-oct image using graph search and tracing of three bench mark reference layers of retina,” in Image Processing (ICIP), 2015 IEEE International Conference on, (IEEE, 2015), pp. 4087–4091.
    [Crossref]
  19. M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
    [Crossref] [PubMed]
  20. M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
    [Crossref]
  21. L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
    [Crossref] [PubMed]
  22. F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
    [Crossref] [PubMed]
  23. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
    [Crossref] [PubMed]
  24. S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Transactions on medical imaging 30, 2126–2133 (2011).
    [Crossref] [PubMed]
  25. G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
    [Crossref]
  26. A. Chakravarty and J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer methods and programs in biomedicine 165, 235–250 (2018).
    [Crossref] [PubMed]
  27. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.
  28. F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), pp. 565–571.
    [Crossref]
  29. Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
    [Crossref] [PubMed]
  30. J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
    [Crossref]

2018 (2)

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

A. Chakravarty and J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer methods and programs in biomedicine 165, 235–250 (2018).
[Crossref] [PubMed]

2017 (3)

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

2016 (2)

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

2015 (5)

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

2014 (1)

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

2013 (4)

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

2011 (1)

S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Transactions on medical imaging 30, 2126–2133 (2011).
[Crossref] [PubMed]

2010 (2)

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

2006 (1)

A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
[Crossref] [PubMed]

2004 (1)

R. N. Weinreb and P. T. Khaw, “Primary open-angle glaucoma,” The Lancet 363, 1711–1720 (2004).
[Crossref]

Abramoff, M. D.

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

Abràmoff, M. D.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Ahmadi, S.-A.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), pp. 565–571.
[Crossref]

AlMobarak, F. A.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Aung, T.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Balasubramanian, M.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Baskaran, M.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Belghith, A.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.

Bhuiyan, A.

M. A. Hussain, A. Bhuiyan, and K. Ramamohanarao, “Disc segmentation and bmo-mrw measurement from sd-oct image using graph search and tracing of three bench mark reference layers of retina,” in Image Processing (ICIP), 2015 IEEE International Conference on, (IEEE, 2015), pp. 4087–4091.
[Crossref]

Bowd, C.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Budenz, D. L.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Burgoyne, C. F.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Cao, X.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Caprioli, J.

A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
[Crossref] [PubMed]

Chakravarty, A.

A. Chakravarty and J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer methods and programs in biomedicine 165, 235–250 (2018).
[Crossref] [PubMed]

Chang, R. T.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Chauhan, B. C.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Chen, Q.

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Chen, Y.

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

Chen, Z.-L.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Cheng, C.-Y.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Cheng, J.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Cheung, C.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Cunefare, D.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

de Sisternes, L.

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Durbin, M. K.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

Fang, L.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Farsiu, S.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Fauser, S.

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

Feuer, W. J.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

Fu, H.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Garvin, M. K.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

Gee, J. C.

Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.

Gendy, M. G.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Girard, M. J.

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

Girkin, C. A.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Gmeiner, J. M.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Goldbaum, M. H.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Guymer, R. H.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Hammel, N.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

Hoyng, C.

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

Hu, Z.

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

Hussain, M. A.

M. A. Hussain, A. Bhuiyan, and K. Ramamohanarao, “Disc segmentation and bmo-mrw measurement from sd-oct image using graph search and tracing of three bench mark reference layers of retina,” in Image Processing (ICIP), 2015 IEEE International Conference on, (IEEE, 2015), pp. 4087–4091.
[Crossref]

Hutchison, D. M.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Hwang, Y. H.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Jeoung, J.

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

Jung, J. J.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Jung, T.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

Khaw, P. T.

R. N. Weinreb and P. T. Khaw, “Primary open-angle glaucoma,” The Lancet 363, 1711–1720 (2004).
[Crossref]

Kim, D.

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

Kim, Y.

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

Kim, Y. Y.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Kruse, F. E.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Kwon, Y. H.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

Laemmer, R.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Lee, J. H.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Lee, K.

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

Leng, T.

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Li, S.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Liebmann, J. M.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Liefers, B.

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

Lin, S.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

Liu, J.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Lu, S.

S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Transactions on medical imaging 30, 2126–2133 (2011).
[Crossref] [PubMed]

Manassakorn, A.

A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
[Crossref] [PubMed]

Mardin, C. Y.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Mari, J. M.

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

Medeiros, F. A.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Milletari, F.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), pp. 565–571.
[Crossref]

Miri, M. S.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Mwanza, J.-C.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Navab, N.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), pp. 565–571.
[Crossref]

Nicolela, M. T.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Niemeijer, M.

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Nouri-Mahdavi, K.

A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
[Crossref] [PubMed]

O’Brien, J.

Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.

O’Leary, N.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Ong, S. H.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Park, K.

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

Park, S. C.

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

Park, Y. M.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Peng, P.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Ramamohanarao, K.

M. A. Hussain, A. Bhuiyan, and K. Ramamohanarao, “Disc segmentation and bmo-mrw measurement from sd-oct image using graph search and tracing of three bench mark reference layers of retina,” in Image Processing (ICIP), 2015 IEEE International Conference on, (IEEE, 2015), pp. 4087–4091.
[Crossref]

Reis, A. S.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Rubin, D. L.

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Sánchez, C. I.

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

Schrems, W. A.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Schrems-Hoesl, L. M.

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

Schreur, V.

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

Sharpe, G. P.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Shen, H.-L.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Shi, W.

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

Sivaswamy, J.

A. Chakravarty and J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer methods and programs in biomedicine 165, 235–250 (2018).
[Crossref] [PubMed]

Sonka, M.

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

Stambolian, D.

Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.

Strouthidis, N. G.

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

Sun, Y.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Tan, N. M.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Tan, N.-M.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Tao, D.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Theelen, T.

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

van Asten, F.

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

van Ginneken, B.

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

Venhuizen, F. G.

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

Wang, C.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Wang, G.

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

Wang, J.-K.

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

Wang, Z.

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

Wei, H.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Weinreb, R. N.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

R. N. Weinreb and P. T. Khaw, “Primary open-angle glaucoma,” The Lancet 363, 1711–1720 (2004).
[Crossref]

A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.

Wong, D. W.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Wong, D. W. K.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Wong, T. Y.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Woo, S.

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Wu, M.

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

Xu, D.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

Xu, Y.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

Yang, H.

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Yang, Z.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

Yin, F.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

Yousefi, S.

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

Zangwill, L. M.

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.

Zhao, R.-C.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Zhao, W.

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

Zheng, Y.

Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.

Zou, B.-J.

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

American Journal of Ophthalmology (1)

A. Manassakorn, K. Nouri-Mahdavi, and J. Caprioli, “Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma,” American Journal of Ophthalmology 141, 105 –115 (2006).
[Crossref] [PubMed]

Biomedical Optics Express (2)

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

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

Biomedical Signal Processing and Control (1)

G. Wang, Z. Wang, Y. Chen, and W. Zhao, “Robust point matching method for multimodal retinal image registration,” Biomedical Signal Processing and Control 19, 68–76 (2015).
[Crossref]

Computer methods and programs in biomedicine (1)

A. Chakravarty and J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer methods and programs in biomedicine 165, 235–250 (2018).
[Crossref] [PubMed]

Eye (1)

Y. Kim, D. Kim, J. Jeoung, D. Kim, and K. Park, “Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa,” Eye 29, 1368 (2015).
[Crossref] [PubMed]

IEEE Transactions on Biomedical Engineering (2)

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering 61, 1143–1154 (2014).
[Crossref] [PubMed]

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic optic disc detection in oct slices via low-rank reconstruction,” IEEE Transactions on Biomedical Engineering 62, 1151–1158 (2015).
[Crossref]

IEEE Transactions on image processing (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing 16, 2080–2095 (2007).
[Crossref] [PubMed]

IEEE Transactions on medical imaging (1)

S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Transactions on medical imaging 30, 2126–2133 (2011).
[Crossref] [PubMed]

M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, and M. K. Garvin, “Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach,” IEEE Transactions on Medical Imaging 34, 1854–1866 (2015).
[Crossref] [PubMed]

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Medical Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Investigative ophthalmology & visual science (2)

Z. Hu, M. D. Abramoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3d spectral optical coherence tomography volumes of the optic nerve head,” Investigative ophthalmology & visual science 51, 5708–5717 (2010).
[Crossref]

J.-C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes,” Investigative Ophthalmology & Visual Science 51, 5724 (2010).
[Crossref]

A. Belghith, C. Bowd, F. A. Medeiros, N. Hammel, Z. Yang, R. N. Weinreb, and L. M. Zangwill, “Does the location of bruch’s membrane opening change over time? longitudinal analysis using san diego automated layer segmentation algorithm (salsa),” Investigative Ophthalmology & Visual Science 57, 675 (2016).
[Crossref]

J. M. Gmeiner, W. A. Schrems, C. Y. Mardin, R. Laemmer, F. E. Kruse, and L. M. Schrems-Hoesl, “Comparison of bruch’s membrane opening minimum rim width and peripapillary retinal nerve fiber layer thickness in early glaucoma assessment,” Investigative Ophthalmology & visual science 57, OCT575 (2016).
[Crossref]

J. M. Mari, N. G. Strouthidis, S. C. Park, and M. J. Girard, “Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation,” Investigative ophthalmology & visual science 54, 2238–2247 (2013).
[Crossref]

Japanese Journal of Ophthalmology (1)

Y. H. Hwang, J. J. Jung, Y. M. Park, Y. Y. Kim, S. Woo, and J. H. Lee, “Effect of myopia and age on optic disc margin anatomy within the parapapillary atrophy area,” Japanese Journal of Ophthalmology 57, 463–470 (2013).
[Crossref] [PubMed]

Journal of Computer Science and Technology (1)

Z.-L. Chen, P. Peng, B.-J. Zou, H.-L. Shen, H. Wei, and R.-C. Zhao, “Automatic anterior lamina cribrosa surface depth measurement based on active contour and energy constraint,” Journal of Computer Science and Technology 32, 1214–1221 (2017).
[Crossref]

Medical image analysis (1)

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, M. Sonka, and M. K. Garvin, “A machine-learning graph-based approach for 3d segmentation of bruch’s membrane opening from glaucomatous sd-oct volumes,” Medical image analysis 39, 206–217 (2017).
[Crossref]

Ophthalmology (1)

B. C. Chauhan, N. O’Leary, F. A. AlMobarak, A. S. Reis, H. Yang, G. P. Sharpe, D. M. Hutchison, M. T. Nicolela, and C. F. Burgoyne, “Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography–derived neuroretinal rim parameter,” Ophthalmology 120, 535–543 (2013).
[Crossref]

Optics Express (1)

M. Wu, T. Leng, L. de Sisternes, D. L. Rubin, and Q. Chen, “Automated segmentation of optic disc in sd-oct images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection,” Optics Express 23, 31216–31229 (2015).
[Crossref] [PubMed]

The Lancet (1)

R. N. Weinreb and P. T. Khaw, “Primary open-angle glaucoma,” The Lancet 363, 1711–1720 (2004).
[Crossref]

Other (7)

Y. Zheng, D. Stambolian, J. O’Brien, and J. C. Gee, “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2013), pp. 75–82.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” arXiv preprint arXiv:1801.00926 (2018).

A. Belghith, C. Bowd, R. N. Weinreb, and L. M. Zangwill, “A hierarchical framework for estimating neuroretinal rim area using 3d spectral domain optical coherence tomography (sd-oct) optic nerve head (onh) images of healthy and glaucoma eyes,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, (IEEE, 2014), pp. 3869–3872.

M. A. Hussain, A. Bhuiyan, and K. Ramamohanarao, “Disc segmentation and bmo-mrw measurement from sd-oct image using graph search and tracing of three bench mark reference layers of retina,” in Image Processing (ICIP), 2015 IEEE International Conference on, (IEEE, 2015), pp. 4087–4091.
[Crossref]

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, (IEEE, 2011), pp. 2626–2629.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), pp. 565–571.
[Crossref]

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Figures (9)

Fig. 1
Fig. 1 Flowchart of the proposed method.
Fig. 2
Fig. 2 Coarse detection. (a) Original SD-OCT images and 2D projection images. (b) Registration between the projection and fundus images. (c) Diagram of the coarse detection, intersection (yellow) (yellow) of the RPE layer (red), and projection line of the disc (green) are the coarse detection results.
Fig. 3
Fig. 3 Data process.(a) ROI determination. (b) Label transformation for different patches.
Fig. 4
Fig. 4 U-net architecture in our proposed method.
Fig. 5
Fig. 5 Illustration of the border tissue and the post-processing steps. (a) The sketch map of ambiguous border tissue. The real BMO is confused by the border tissue in this case. (b) The sketch map of post-processing. There are four patches (the box in the ROI indicate the upper left patch) extracted in the ROI and send to the trained U-net, the mask of patches with maximum Mi is selected as the result and its center point is the final result of BMO.
Fig. 6
Fig. 6 Results of various radius parameters.
Fig. 7
Fig. 7 Result of various loss function components. (a) Results of group 4 (green), group 2 (yellow), and the ground truth (red). (b) Results of group 4 (green), group 3 (pink), and the ground truth (red). Best viewed in color.
Fig. 8
Fig. 8 Evaluation of the effect of post-processing. The dots in red, green, and yellow represent the ground truth, the result with post-processing, and the result without post-processing, respectively. (a) Result of the method without post-processing is satisfactory. (b) Result of the method without post-processing has a large deviation. Best viewed in color.
Fig. 9
Fig. 9 Comparison of our proposed method (yellow) with the ground truth (red) in the 2D projection image.

Tables (3)

Tables Icon

Table 1 Evaluation of the effect of loss function components

Tables Icon

Table 2 Evaluation of the effect of post-processing

Tables Icon

Table 3 Results of BMO detection

Equations (5)

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

D L = 1 2 x Ω p x g x x Ω p x 2 x Ω g x 2
A L = 1 m i n ( x Ω p x , x Ω g x ) m a x ( x Ω p x , x Ω g x )
L o s s = D L + A L + M S E   = 2 2 x Ω p x g x x Ω p x 2 x Ω g x 2 m i n ( x Ω p x , x Ω g x ) m a x ( x Ω p x , x Ω g x ) + 1 n x Ω ( p x g x ) 2
M i = j = 1 , i j m D i c e ( S i , S j ) k
C = C e n t e r ( S d ) , d = arg max  i M i , i = 1 , 2 m

Metrics