Abstract

A deep-learning (DL) based noise reduction algorithm, in combination with a vessel shadow compensation method and a three-dimensional (3D) segmentation technique, has been developed to achieve, to the authors best knowledge, the first automatic segmentation of the anterior surface of the lamina cribrosa (LC) in volumetric ophthalmic optical coherence tomography (OCT) scans. The present DL-based OCT noise reduction algorithm was trained without the need of noise-free ground truth images by utilizing the latest development in deep learning of de-noising from single noisy images, and was demonstrated to be able to cover more locations in the retina and disease cases of different types to achieve high robustness. Compared with the original single OCT images, a 6.6 dB improvement in peak signal-to-noise ratio and a 0.65 improvement in the structural similarity index were achieved. The vessel shadow compensation method analyzes the energy profile in each A-line and automatically compensates the pixel intensity of locations underneath the detected blood vessel. Combining the noise reduction algorithm and the shadow compensation and contrast enhancement technique, medical experts were able to identify the anterior surface of the LC in 98.3% of the OCT images. The 3D segmentation algorithm employs a two-round procedure based on gradients information and information from neighboring images. An accuracy of 90.6% was achieved in a validation study involving 180 individual B-scans from 36 subjects, compared to 64.4% in raw images. This imaging and analysis strategy enables the first automatic complete view of the anterior LC surface, to the authors best knowledge, which may have the potentials in new LC parameters development for glaucoma diagnosis and management.

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

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2019 (1)

N. Y. Tan, Y.-C. Tham, S. G. Thakku, X. Wang, M. Baskaran, M. C. Tan, J.-M. Mari, N. G. Strouthidis, T. Aung, and M. J. Girard, “Changes in the anterior lamina cribrosa morphology with glaucoma severity,” Sci. Rep. 9(1), 6612 (2019).
[Crossref]

2018 (5)

A. Ha, T. J. Kim, M. J. Girard, J. M. Mari, Y. K. Kim, K. H. Park, and J. W. Jeoung, “Baseline lamina cribrosa curvature and subsequent visual field progression rate in primary open-angle glaucoma,” Ophthalmology 125(12), 1898–1906 (2018).
[Crossref]

K. Zhang, W. Zuo, and L. Zhang, “Ffdnet: Toward a fast and flexible solution for cnn based image denoising,” IEEE Trans. on Image Process. 27(9), 4608–4622 (2018).
[Crossref]

M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
[Crossref]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cgan,” Biomed. Opt. Express 9(11), 5129–5146 (2018).
[Crossref]

K. J. Halupka, B. J. Antony, M. H. Lee, K. A. Lucy, R. S. Rai, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garnavi, “Retinal optical coherence tomography image enhancement via deep learning,” Biomed. Opt. Express 9(12), 6205–6221 (2018).
[Crossref]

2017 (5)

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
[Crossref]

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

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. on Image Process. 26(7), 3142–3155 (2017).
[Crossref]

J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Išgum, “Generative adversarial networks for noise reduction in low-dose ct,” IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017).
[Crossref]

S. H. Lee, T.-W. Kim, E. J. Lee, M. J. Girard, and J. M. Mari, “Diagnostic power of lamina cribrosa depth and curvature in glaucoma,” Invest. Ophthalmol. Visual Sci. 58(2), 755–762 (2017).
[Crossref]

2015 (5)

S. G. Thakku, Y.-C. Tham, M. Baskaran, J.-M. Mari, N. G. Strouthidis, T. Aung, C.-Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Visual Sci. 56(6), 3604–3614 (2015).
[Crossref]

A. Miki, Y. Ikuno, T. Asai, S. Usui, and K. Nishida, “Defects of the lamina cribrosa in high myopia and glaucoma,” PLoS One 10(9), e0137909 (2015).
[Crossref]

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina cribrosa depth in different stages of glaucoma,” Invest. Ophthalmol. Visual Sci. 56(3), 2059–2064 (2015).
[Crossref]

E. J. Lee, T.-W. Kim, M. Kim, and H. Kim, “Influence of lamina cribrosa thickness and depth on the rate of progressive retinal nerve fiber layer thinning,” Ophthalmology 122(4), 721–729 (2015).
[Crossref]

D. Kaba, Y. Wang, C. Wang, X. Liu, H. Zhu, A. Salazar-Gonzalez, and Y. Li, “Retina layer segmentation using kernel graph cuts and continuous max-flow,” Opt. Express 23(6), 7366–7384 (2015).
[Crossref]

2014 (5)

H. Danesh, R. Kafieh, H. Rabbani, and F. Hajizadeh, “Segmentation of choroidal boundary in enhanced depth imaging OCTS using a multiresolution texture based modeling in graph cuts,” Comput. Math. Method M. 2014, 1–9 (2014).
[Crossref]

I. A. Sigal, B. Wang, N. G. Strouthidis, T. Akagi, and M. J. Girard, “Recent advances in OCT imaging of the lamina cribrosa,” Br. J. Ophthalmol. 98(Suppl 2), ii34–ii39 (2014).
[Crossref]

A. J. Tatham, A. Miki, R. N. Weinreb, L. M. Zangwill, and F. A. Medeiros, “Defects of the lamina cribrosa in eyes with localized retinal nerve fiber layer loss,” Ophthalmology 121(1), 110–118 (2014).
[Crossref]

O. S. Faridi, S. C. Park, R. Kabadi, D. Su, C. G. De Moraes, J. M. Liebmann, and R. Ritch, “Effect of focal lamina cribrosa defect on glaucomatous visual field progression,” Ophthalmology 121(8), 1524–1530 (2014).
[Crossref]

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The pathophysiology and treatment of glaucoma: a review,” JAMA 311(18), 1901–1911 (2014).
[Crossref]

2013 (1)

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,” Invest. Ophthalmol. Visual Sci. 54(3), 2238–2247 (2013).
[Crossref]

2012 (1)

S. Kiumehr, S. C. Park, S. Dorairaj, C. C. Teng, C. Tello, J. M. Liebmann, and R. Ritch, “In vivo evaluation of focal lamina cribrosa defects in glaucoma,” Arch. Ophthalmol. 130(5), 552–559 (2012).
[Crossref]

2011 (2)

M. J. Girard, N. G. Strouthidis, C. R. Ethier, and J. M. Mari, “Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head,” Invest. Ophthalmol. Visual Sci. 52(10), 7738–7748 (2011).
[Crossref]

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(9), 2493–2503 (2011).
[Crossref]

2010 (2)

2009 (4)

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

T. Fabritius, S. Makita, Y. Hong, R. A. Myllylä, and Y. Yasuno, “Automated retinal shadow compensation of optical coherence tomography images,” J. Biomed. Opt. 14(1), 010503 (2009).
[Crossref]

M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
[Crossref]

R. Inoue, M. Hangai, Y. Kotera, H. Nakanishi, S. Mori, S. Morishita, and N. Yoshimura, “Three-dimensional high-speed optical coherence tomography imaging of lamina cribrosa in glaucoma,” Ophthalmology 116(2), 214–222 (2009).
[Crossref]

2007 (3)

H. Yang, J. C. Downs, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “3-d histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness,” Invest. Ophthalmol. Visual Sci. 48(10), 4597–4607 (2007).
[Crossref]

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Visual Sci. 48(7), 3195–3208 (2007).
[Crossref]

P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type ii fuzzy set,” Opt. Express 15(24), 15747–15758 (2007).
[Crossref]

2006 (2)

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124(2), 193–198 (2006).
[Crossref]

H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol. 90(3), 262–267 (2006).
[Crossref]

2004 (2)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
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D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett. 29(24), 2878–2880 (2004).
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1999 (1)

J. M. Schmitt, S. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–106 (1999).
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1997 (1)

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1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
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1983 (1)

H. A. Quigley, R. M. Hohman, E. M. Addicks, R. W. Massof, and W. R. Green, “Morphologic changes in the lamina cribrosa correlated with neural loss in open-angle glaucoma,” Am. J. Ophthalmol. 95(5), 673–691 (1983).
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1981 (1)

H. A. Quigley, E. M. Addicks, W. R. Green, and A. Maumenee, “Optic nerve damage in human glaucoma: II. the site of injury and susceptibility to damage,” Arch. Ophthalmol. 99(4), 635–649 (1981).
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1969 (1)

D. R. Anderson, “Ultrastructure of human and monkey lamina cribrosa and optic nerve head,” Arch. Ophthalmol. 82(6), 800–814 (1969).
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M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging. 28(9), 1436–1447 (2009).
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H. A. Quigley, R. M. Hohman, E. M. Addicks, R. W. Massof, and W. R. Green, “Morphologic changes in the lamina cribrosa correlated with neural loss in open-angle glaucoma,” Am. J. Ophthalmol. 95(5), 673–691 (1983).
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H. A. Quigley, E. M. Addicks, W. R. Green, and A. Maumenee, “Optic nerve damage in human glaucoma: II. the site of injury and susceptibility to damage,” Arch. Ophthalmol. 99(4), 635–649 (1981).
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J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila, “Noise2noise: Learning image restoration without clean data,” arXiv preprint arXiv:1803.04189 (2018).

Akagi, T.

I. A. Sigal, B. Wang, N. G. Strouthidis, T. Akagi, and M. J. Girard, “Recent advances in OCT imaging of the lamina cribrosa,” Br. J. Ophthalmol. 98(Suppl 2), ii34–ii39 (2014).
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D. R. Anderson, “Ultrastructure of human and monkey lamina cribrosa and optic nerve head,” Arch. Ophthalmol. 82(6), 800–814 (1969).
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M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
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S. G. Thakku, Y.-C. Tham, M. Baskaran, J.-M. Mari, N. G. Strouthidis, T. Aung, C.-Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Visual Sci. 56(6), 3604–3614 (2015).
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H. Yang, J. C. Downs, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “3-d histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness,” Invest. Ophthalmol. Visual Sci. 48(10), 4597–4607 (2007).
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J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Visual Sci. 48(7), 3195–3208 (2007).
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M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
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M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
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C. A. Toth, D. G. Narayan, S. A. Boppart, M. R. Hee, J. G. Fujimoto, R. Birngruber, C. P. Cain, C. D. DiCarlo, and W. P. Roach, “A comparison of retinal morphology viewed by optical coherence tomography and by light microscopy,” Arch. Ophthalmol. 115(11), 1425–1428 (1997).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
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A. Belghith, C. Bowd, F. A. Medeiros, R. N. Weinreb, and L. M. Zangwill, “Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes?” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 222–225.

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M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
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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.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International conference on medical image computing and computer-assisted intervention, (Springer, 2016), pp. 424–432.

Brumm, J.

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina cribrosa depth in different stages of glaucoma,” Invest. Ophthalmol. Visual Sci. 56(3), 2059–2064 (2015).
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M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
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J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Visual Sci. 48(7), 3195–3208 (2007).
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H. Yang, J. C. Downs, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “3-d histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness,” Invest. Ophthalmol. Visual Sci. 48(10), 4597–4607 (2007).
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M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging. 28(9), 1436–1447 (2009).
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Cain, C. P.

C. A. Toth, D. G. Narayan, S. A. Boppart, M. R. Hee, J. G. Fujimoto, R. Birngruber, C. P. Cain, C. D. DiCarlo, and W. P. Roach, “A comparison of retinal morphology viewed by optical coherence tomography and by light microscopy,” Arch. Ophthalmol. 115(11), 1425–1428 (1997).
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Canny, J.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
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Chan, A.

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124(2), 193–198 (2006).
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Chan, K.

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Chen, M.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis, (Springer, 2017), pp. 177–184.

Chen, X.

Chen, Y.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. on Image Process. 26(7), 3142–3155 (2017).
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S. G. Thakku, Y.-C. Tham, M. Baskaran, J.-M. Mari, N. G. Strouthidis, T. Aung, C.-Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Visual Sci. 56(6), 3604–3614 (2015).
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Cheng, X.

Chiu, S. J.

Çiçek, Ö.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International conference on medical image computing and computer-assisted intervention, (Springer, 2016), pp. 424–432.

Culley, S.

M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
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O. S. Faridi, S. C. Park, R. Kabadi, D. Su, C. G. De Moraes, J. M. Liebmann, and R. Ritch, “Effect of focal lamina cribrosa defect on glaucomatous visual field progression,” Ophthalmology 121(8), 1524–1530 (2014).
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Dibrov, A.

M. Weigert, U. Schmidt, T. Boothe, M. Andreas, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, and S. Culley, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
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C. A. Toth, D. G. Narayan, S. A. Boppart, M. R. Hee, J. G. Fujimoto, R. Birngruber, C. P. Cain, C. D. DiCarlo, and W. P. Roach, “A comparison of retinal morphology viewed by optical coherence tomography and by light microscopy,” Arch. Ophthalmol. 115(11), 1425–1428 (1997).
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S. Kiumehr, S. C. Park, S. Dorairaj, C. C. Teng, C. Tello, J. M. Liebmann, and R. Ritch, “In vivo evaluation of focal lamina cribrosa defects in glaucoma,” Arch. Ophthalmol. 130(5), 552–559 (2012).
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Downs, J. C.

M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
[Crossref]

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Visual Sci. 48(7), 3195–3208 (2007).
[Crossref]

H. Yang, J. C. Downs, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “3-d histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness,” Invest. Ophthalmol. Visual Sci. 48(10), 4597–4607 (2007).
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Duker, J. S.

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124(2), 193–198 (2006).
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Ethier, C. R.

M. J. Girard, N. G. Strouthidis, C. R. Ethier, and J. M. Mari, “Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head,” Invest. Ophthalmol. Visual Sci. 52(10), 7738–7748 (2011).
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O. S. Faridi, S. C. Park, R. Kabadi, D. Su, C. G. De Moraes, J. M. Liebmann, and R. Ritch, “Effect of focal lamina cribrosa defect on glaucomatous visual field progression,” Ophthalmology 121(8), 1524–1530 (2014).
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Fauser, S.

Fischer, P.

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

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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J. Schuman, C. Puliafito, and J. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK Incorporated, 2004).

Fujimoto, J. G.

A. Chan, J. S. Duker, T. H. Ko, J. G. Fujimoto, and J. S. Schuman, “Normal macular thickness measurements in healthy eyes using stratus optical coherence tomography,” Arch. Ophthalmol. 124(2), 193–198 (2006).
[Crossref]

D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett. 29(24), 2878–2880 (2004).
[Crossref]

C. A. Toth, D. G. Narayan, S. A. Boppart, M. R. Hee, J. G. Fujimoto, R. Birngruber, C. P. Cain, C. D. DiCarlo, and W. P. Roach, “A comparison of retinal morphology viewed by optical coherence tomography and by light microscopy,” Arch. Ophthalmol. 115(11), 1425–1428 (1997).
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Fukuma, Y.

Furlanetto, R. L.

S. C. Park, J. Brumm, R. L. Furlanetto, C. Netto, Y. Liu, C. Tello, J. M. Liebmann, and R. Ritch, “Lamina cribrosa depth in different stages of glaucoma,” Invest. Ophthalmol. Visual Sci. 56(3), 2059–2064 (2015).
[Crossref]

Garnavi, R.

Garvin, M. K.

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

Gee, J. C.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis, (Springer, 2017), pp. 177–184.

Girard, M. J.

N. Y. Tan, Y.-C. Tham, S. G. Thakku, X. Wang, M. Baskaran, M. C. Tan, J.-M. Mari, N. G. Strouthidis, T. Aung, and M. J. Girard, “Changes in the anterior lamina cribrosa morphology with glaucoma severity,” Sci. Rep. 9(1), 6612 (2019).
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A. Ha, T. J. Kim, M. J. Girard, J. M. Mari, Y. K. Kim, K. H. Park, and J. W. Jeoung, “Baseline lamina cribrosa curvature and subsequent visual field progression rate in primary open-angle glaucoma,” Ophthalmology 125(12), 1898–1906 (2018).
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S. G. Thakku, Y.-C. Tham, M. Baskaran, J.-M. Mari, N. G. Strouthidis, T. Aung, C.-Y. Cheng, and M. J. Girard, “A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy indian eyes,” Invest. Ophthalmol. Visual Sci. 56(6), 3604–3614 (2015).
[Crossref]

I. A. Sigal, B. Wang, N. G. Strouthidis, T. Akagi, and M. J. Girard, “Recent advances in OCT imaging of the lamina cribrosa,” Br. J. Ophthalmol. 98(Suppl 2), ii34–ii39 (2014).
[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,” Invest. Ophthalmol. Visual Sci. 54(3), 2238–2247 (2013).
[Crossref]

M. J. Girard, N. G. Strouthidis, C. R. Ethier, and J. M. Mari, “Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head,” Invest. Ophthalmol. Visual Sci. 52(10), 7738–7748 (2011).
[Crossref]

Girkin, C.

H. Yang, J. C. Downs, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “3-d histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness,” Invest. Ophthalmol. Visual Sci. 48(10), 4597–4607 (2007).
[Crossref]

J. C. Downs, H. Yang, C. Girkin, L. Sakata, A. Bellezza, H. Thompson, and C. F. Burgoyne, “Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture,” Invest. Ophthalmol. Visual Sci. 48(7), 3195–3208 (2007).
[Crossref]

Grau, V.

M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
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Green, W. R.

H. A. Quigley, R. M. Hohman, E. M. Addicks, R. W. Massof, and W. R. Green, “Morphologic changes in the lamina cribrosa correlated with neural loss in open-angle glaucoma,” Am. J. Ophthalmol. 95(5), 673–691 (1983).
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H. A. Quigley, E. M. Addicks, W. R. Green, and A. Maumenee, “Optic nerve damage in human glaucoma: II. the site of injury and susceptibility to damage,” Arch. Ophthalmol. 99(4), 635–649 (1981).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Grimm, J.

M. D. Roberts, V. Grau, J. Grimm, J. Reynaud, A. J. Bellezza, C. F. Burgoyne, and J. C. Downs, “Remodeling of the connective tissue microarchitecture of the lamina cribrosa in early experimental glaucoma,” Invest. Ophthalmol. Visual Sci. 50(2), 681–690 (2009).
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Figures (12)

Fig. 1.
Fig. 1. Definition of different image types used in this study. Top left: B-scans repeated 128x at the same location; top right: registered and averaged B-scan of the 128x repeats; bottom left: one of the B-scan images from the 128 repeats; bottom right: DL noise-reduced image of the bottom left image.
Fig. 2.
Fig. 2. Qualitative evaluation examples. (A) single B-scan; (B) median filtered image with a $5\times 5$ sized filter; (C) DL based noise reduced image; (D) registered and averaged image of B-scan repeated 128× at the same location. For detailed inspection, (E), (F), (G), and (H) show the zoom-ins of the areas highlighted by the green boxes in (A), (B), (C), and (D) respectively.
Fig. 3.
Fig. 3. Comparison of $6\times 6~mm^2$ en-face images of choroidal structures before and after noise reduction. (A) noise-reduced B-scan after flattening, the yellow line indicates the depths of en-face images (B) and (C); (B) en-face image extracted from the original volume corresponding to the depth in (A); (C) en-face image extracted from the noise-reduced volume corresponding to the depth in (A) with the red line indicating where B-frame (A) was extracted from; (D) zoom-in version of (B) for detail investigations; (E) zoom-in version of (C) for detail investigations.
Fig. 4.
Fig. 4. Comparison of $6\times 6~mm^2$ en-face images of LC structures before and after noise reduction. (A) noise-reduced B-frame with the yellow line indicating where the subsequent en-face images are extracted from; (B) en-face images extracted from the original volume at depth highlighted in (A); (C) en-face images extracted from the noise-reduced volume at depth highlighted in (A) with the red line indicating where B-frame (A) was extracted from; (D) zoom-in version of (B) for detail investigations; (E) zoom-in version of (C) for detail investigations.
Fig. 5.
Fig. 5. (A) OCT image with shadows observed underneath the blood vessels (highlighted with arrows); (B) energy profile across B-scan, where the blue curve depicts the original energy profile containing high-frequency random noise and energy dip due to shadow, and the red curve shows low-pass-filtered energy profile.
Fig. 6.
Fig. 6. Detection of the structure region of an OCT A-line. Intersections of the cutoff level (purple line) and the moving average energy profile (red line) defines the structure region (shaded area).
Fig. 7.
Fig. 7. Comparison between (A) the original and (B) compensated images. Yellow lines in the original image mark the segmented starting and ending point of the compensation. Narrower vessels (red, right-pointing arrow) are compensated better than wide vessel (green, left-pointing arrow).
Fig. 8.
Fig. 8. (A) A sample B-scan extracted from a 3D volume; (B) the shadow-compensated results of the B-scan.
Fig. 9.
Fig. 9. (A) Bright-band artifact (enclosed in red box) is common with the adopted compensation method [25], where the bright line can cut through LC structure and obscure the anterior border; (B) Adjusting contrast to reduce noise-level before the compensation reduces the bright-artifact and further improves the contrast of LC border.
Fig. 10.
Fig. 10. A flow chart of the proposed two-round segmentation.
Fig. 11.
Fig. 11. Comparison of LC segmentation results using (A) original and (B) enhanced B-scan images of the same subject. The green dotted line is the manual segmentation results by medical experts and the red dash-dotted lines are the automated segmentation results from our algorithm.
Fig. 12.
Fig. 12. Example 3D LC anterior depth surface. (A) LC anterior surface from 3D segmentation, color coded with depth information; (B) 2D visualization of the depth map with blue indicating an inwards curvature into the ONH.

Tables (3)

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Table 1. PSNR and SSIM results from 3 scans at different locations of the eye.

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Table 2. LC segmentation accuracy results

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Table 3. The average absolute differences between the automatic segmentation boundaries and ground truth segmentation boundaries measured after different enhancement stages

Equations (11)

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total loss = i L ( f θ ( x ^ i ) , y i )
total loss = i L ( f θ ( x ^ i ) , y ^ i ) ,
MSE = 1 m × n i = 0 m 1 j = 0 n 1 [ R ( i , j ) I ( i , j ) ] 2 .
PSNR = 20 log 10 ( MAX I ) 10 log 10 ( MSE ) ,
SSIM ( R , I ) = l ( R , I ) c ( R , I ) s ( R , I ) ,
l ( R , I ) = 2 μ R μ I + c 1 μ R 2 + μ I 2 + c 1 , c ( R , I ) = 2 σ R σ I + c 2 σ R 2 + σ I 2 + c 2 , s ( R , I ) = 2 σ R I + c 3 σ R σ I + c 3 ,
E i , j = I i , j n , ( i = 1 , 2 , , N ; j = 1 , 2 , , D )
E i = j = 1 D E i , j = j = 1 D I i , j n .
C ( i , j , k ) = w 1 Edge ( i , j , k ) + w 2 Gradient V ( i , j , k ) + w 3 Gradient H ( i , j , k )
acc ( i , j , k ) | k = k 0 = { , j < 1   or   j > m . C ( i , j , k 0 ) , i = n . min s = j u : j + u acc ( i 1 , s , k 0 ) + C ( i , j , k 0 ) , otherwise .
conf k 0 = 1 1 n p a b s ( j p j f )