Abstract

Optical coherence tomography (OCT) images of the retina are a powerful tool for diagnosing and monitoring eye disease. However, they are plagued by speckle noise, which reduces image quality and reliability of assessment. This paper introduces a novel speckle reduction method inspired by the recent successes of deep learning in medical imaging. We present two versions of the network to reflect the needs and preferences of different end-users. Specifically, we train a convolution neural network to denoise cross-sections from OCT volumes of healthy eyes using either (1) mean-squared error, or (2) a generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. We then interrogate the success of both methods with extensive quantitative and qualitative metrics on cross-sections from both healthy and glaucomatous eyes. The results show that the former approach provides state-of-the-art improvement in quantitative metrics such as PSNR and SSIM, and aids layer segmentation. However, the latter approach, which puts more weight on visual perception, outperformed for qualitative comparisons based on accuracy, clarity, and personal preference. Overall, our results demonstrate the effectiveness and efficiency of a deep learning approach to denoising OCT images, while maintaining subtle details in the images.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

Yuhui Ma, Xinjian Chen, Weifang Zhu, Xuena Cheng, Dehui Xiang, and Fei Shi
Biomed. Opt. Express 9(11) 5129-5146 (2018)

Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases

Acner Camino, Zhuo Wang, Jie Wang, Mark E. Pennesi, Paul Yang, David Huang, Dengwang Li, and Yali Jia
Biomed. Opt. Express 9(7) 3092-3105 (2018)

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans

Zaixing Mao, Atsuya Miki, Song Mei, Ying Dong, Kazuichi Maruyama, Ryo Kawasaki, Shinichi Usui, Kenji Matsushita, Kohji Nishida, and Kinpui Chan
Biomed. Opt. Express 10(11) 5832-5851 (2019)

References

  • View by:
  • |
  • |
  • |

  1. C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
    [Crossref] [PubMed]
  2. F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139, 44–55 (2005).
    [Crossref] [PubMed]
  3. V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
    [Crossref] [PubMed]
  4. S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
    [Crossref]
  5. S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
    [Crossref]
  6. S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
    [Crossref] [PubMed]
  7. T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
    [Crossref]
  8. A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
    [Crossref]
  9. S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
    [Crossref]
  10. H. Yu, J. Gao, and A. Li, “Probability-based non-local means filter for speckle noise suppression in optical coherence tomography images,” Opt. Lett. 41, 994 (2016).
    [Crossref] [PubMed]
  11. J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
    [Crossref]
  12. L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
    [Crossref] [PubMed]
  13. L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
    [Crossref]
  14. Z. Jian, Z. Yu, L. Yu, B. Rao, Z. Chen, and B. J. Tromberg, “Speckle attenuation in optical coherence tomography by curvelet shrinkage,” Opt. Lett. 34, 1516 (2009).
    [Crossref] [PubMed]
  15. J. Xu, H. Ou, E. Y. Lam, P. C. Chui, and K. K. Y. Wong, “Speckle reduction of retinal optical coherence tomography based on contourlet shrinkage,” Opt. Lett. 38, 2900 (2013).
    [Crossref] [PubMed]
  16. M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3, 572 (2012).
    [Crossref] [PubMed]
  17. J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.
  18. J. Aum, J.-h. Kim, and J. Jeong, “Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian anisotropic kernels,” Appl. Opt. 54, D43 (2015).
    [Crossref]
  19. D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.
  20. L. Gondara, “Medical image denoising using convolutional denoising autoencoders,” in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), (IEEE, 2016), pp. 241–246.
    [Crossref]
  21. Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
    [Crossref]
  22. H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
    [Crossref]
  23. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.
  24. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of The 34th International Conference on Machine Learning, (2017), pp. 1–32.
  25. C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.
  26. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, (2014).
  27. Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
    [Crossref]
  28. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.
  29. S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
    [Crossref]
  30. D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” \International Conference on Learning Representations (2014).
  31. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
    [Crossref]
  32. I. W. Selesnick, “The double-density dual-tree DWT,” IEEE Transactions on Signal Processing, 52 (2004), pp. 1304–1314.
    [Crossref]
  33. S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
    [Crossref] [PubMed]
  34. J. D. Gibbons and S. Chakraborti, Nonparametric Atatistical Inference (Chapman & Hall/Taylor & Francis, 2003).
  35. M. L. McHugh, “Interrater reliability: the kappa statistic,” Biochem. Medica 22, 276–282 (2012).
    [Crossref]
  36. A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
    [Crossref] [PubMed]
  37. K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
    [Crossref]
  38. M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
    [Crossref]
  39. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413 (2010).
    [Crossref] [PubMed]
  40. B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
    [Crossref]
  41. A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.
  42. K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
    [Crossref]

2018 (1)

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

2017 (2)

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

2016 (2)

H. Yu, J. Gao, and A. Li, “Probability-based non-local means filter for speckle noise suppression in optical coherence tomography images,” Opt. Lett. 41, 994 (2016).
[Crossref] [PubMed]

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

2015 (1)

2014 (1)

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

2013 (1)

2012 (8)

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3, 572 (2012).
[Crossref] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
[Crossref]

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

M. L. McHugh, “Interrater reliability: the kappa statistic,” Biochem. Medica 22, 276–282 (2012).
[Crossref]

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

2011 (1)

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

2010 (3)

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

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

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

2009 (3)

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Z. Jian, Z. Yu, L. Yu, B. Rao, Z. Chen, and B. J. Tromberg, “Speckle attenuation in optical coherence tomography by curvelet shrinkage,” Opt. Lett. 34, 1516 (2009).
[Crossref] [PubMed]

2007 (2)

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

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

2006 (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

2005 (1)

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

2000 (2)

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
[Crossref]

Abramoff, M. D.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

Abràmoff, M. D.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Acosta, A.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Ahmed, F.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

Aitken, A.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Antony, B.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Arditi, M.

D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.

Arjovsky, M.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of The 34th International Conference on Machine Learning, (2017), pp. 1–32.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

Aum, J.

Ba, J.

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” \International Conference on Learning Representations (2014).

Bai, L.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

Besson, A.

D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.

Bilenca, A.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Boretsky, A. R.

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

Borsdorf, A.

Bottou, L.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of The 34th International Conference on Machine Learning, (2017), pp. 1–32.

Bouma, B. E.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Bowd, C.

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

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Burgoyne, C. F.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Burns, T. L.

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Caballero, J.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Calabresi, P.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Carass, A.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Carvalho, M.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Chakraborti, S.

J. D. Gibbons and S. Chakraborti, Nonparametric Atatistical Inference (Chapman & Hall/Taylor & Francis, 2003).

Chauhan, B. C.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Chen, D. Z.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

Chen, H.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Chen, Y.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Chen, Z.

Chintala, S.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of The 34th International Conference on Machine Learning, (2017), pp. 1–32.

Chitchian, S.

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

Chiu, S. J.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

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

Chiu, T.

S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
[Crossref]

Christensen, U.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

Chui, P. C.

Contreras Ortiz, S. H.

S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
[Crossref]

Courville, A.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

Cunefare, D.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

Cunningham, A.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

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 Process. 16, 2080–2095 (2007).
[Crossref]

Desjardins, A. E.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Duan, J.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

Duker, J. S.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Dumoulin, V.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

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 Process. 16, 2080–2095 (2007).
[Crossref]

Fang, L.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

Farsiu, S.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

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

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

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 Process. 16, 2080–2095 (2007).
[Crossref]

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

Fox, M. D.

S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
[Crossref]

Fujimoto, J. G.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Gao, J.

Garvin, M. K.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Gibbons, J. D.

J. D. Gibbons and S. Chakraborti, Nonparametric Atatistical Inference (Chapman & Hall/Taylor & Francis, 2003).

Gondara, L.

L. Gondara, “Medical image denoising using convolutional denoising autoencoders,” in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), (IEEE, 2016), pp. 241–246.
[Crossref]

Gottlob, I.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

Guibas, L. J.

Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
[Crossref]

Gulrajani, I.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.

Hornegger, J.

Huszar, F.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Izatt, J. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

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

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Jansonius, N. M.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Jeong, J.

Jian, Z.

Jørgensen, T. M.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

Jung, S. ho

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Kalra, M. K.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

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 Process. 16, 2080–2095 (2007).
[Crossref]

Kim, J.-h.

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” \International Conference on Learning Representations (2014).

Klein, S.

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Ko, T. H.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Koreishi, A. F.

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Kowalczyk, A.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Kwon, Y. H.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

Lam, E. Y.

Lang, A.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Ledig, C.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Lee, K.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

Li, A.

Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

Li, S.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

Li, X. T.

Liao, P.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Lin, F.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Lu, W.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

Mardin, C. Y.

Mayer, M. A.

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3, 572 (2012).
[Crossref] [PubMed]

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

McHugh, M. L.

M. L. McHugh, “Interrater reliability: the kappa statistic,” Biochem. Medica 22, 276–282 (2012).
[Crossref]

Medeiros, F. A.

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

Motamedi, M.

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

Mou, X.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

Murphy, K.

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Nicholas, P.

Nicolela, M. T.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Nie, Q.

Niemeijer, M.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

O’Connell, R. V.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

Ou, H.

Ozcan, A.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Perdios, D.

D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.

Pluim, J. P.

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Prince, J. L.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Proudlock, F.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

Ramdas, W. D.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Rao, B.

Reis, A. S.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.

Rubner, Y.

Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
[Crossref]

Russell, S. R.

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Samani, N. N.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

Sander, B.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

Schuman, J. S.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Schuman, S. G.

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Selesnick, I. W.

I. W. Selesnick, “The double-density dual-tree DWT,” IEEE Transactions on Signal Processing, 52 (2004), pp. 1304–1314.
[Crossref]

Sharpe, G. P.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Shi, W.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Shi, Y.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, (2014).

Soliman, W.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

Sonka, M.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

Sotirchos, E.

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Srinivasan, V. J.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Staring, M.

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.

Susanna, R.

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

Tang, L.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Tearney, G. J.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Tejani, A.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Tench, C.

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

Theis, L.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Thiran, J.-P.

D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.

Thomadsen, J.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

Tomasi, C.

Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
[Crossref]

Tornow, R. P.

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012).
[Crossref] [PubMed]

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

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

Totz, J.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Tromberg, B. J.

van Kuijk, F. J.

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

Vessani, R. M.

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

Viergever, M. A.

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Vingerling, J. R.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Wagner, M.

Wang, G.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Wang, Z.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

Weinreb, R. N.

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

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Williams, J. M.

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Winter, K. P.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

Witkin, A. J.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Wojtkowski, M.

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

Wong, K. K. Y.

Wu, X.

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

Xu, J.

Yan, P.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

Yang, H.

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Yang, Q.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

Yu, H.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

H. Yu, J. Gao, and A. Li, “Probability-based non-local means filter for speckle noise suppression in optical coherence tomography images,” Opt. Lett. 41, 994 (2016).
[Crossref] [PubMed]

Yu, L.

Yu, Z.

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

Zangwill, L. M.

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

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.

Zhang, Y.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Zhou, J.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, (2014).

Am. J. Ophthalmol. (1)

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

Appl. Opt. (1)

Arch. Ophthalmol. (1)

C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol. 118, 22–26 (2000).
[Crossref] [PubMed]

Biochem. Medica (1)

M. L. McHugh, “Interrater reliability: the kappa statistic,” Biochem. Medica 22, 276–282 (2012).
[Crossref]

Biomed. Opt. Express (2)

Biomed. optics express (1)

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. optics express 2, 2403–2416 (2011).
[Crossref]

Biomed. Signal Process. Control. (2)

J. Duan, W. Lu, C. Tench, I. Gottlob, F. Proudlock, N. N. Samani, and L. Bai, “Denoising optical coherence tomography using second order total generalized variation decomposition,” Biomed. Signal Process. Control. 24, 120–127 (2016).
[Crossref]

S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control. 7, 419–428 (2012).
[Crossref]

IEEE Transactions on Image Process. (1)

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

IEEE Transactions on Med. Imaging (6)

S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: A toolbox for intensity-based medical image registration,” IEEE Transactions on Med. Imaging 29, 196–205 (2010).
[Crossref]

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, and G. Wang, “Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Med. Imaging 37, 1348–1357 (2018).
[Crossref]

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN),” IEEE Transactions on Med. Imaging 36, 2524–2535 (2017).
[Crossref]

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation based sparse reconstruction of optical coherence tomography images,” IEEE Transactions on Med. Imaging 36, 407–421 (2017).
[Crossref]

M. K. Garvin, M. D. Abràmoff, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images,” IEEE Transactions on Med. Imaging 28, 1436–1447 (2009).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Transactions on Med. Imaging 29, 159–168 (2010).
[Crossref]

IEEE Transactions on Pattern Analysis Mach. Intell. (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Transactions on Pattern Analysis Mach. Intell. 28, 119–134 (2006).
[Crossref]

Int. J. Comput. Vis. (1)

Y. Rubner, C. Tomasi, and L. J. Guibas, “Earth mover’s distance as a metric for image retrieval,” Int. J. Comput. Vis. 40, 99–121 (2000).
[Crossref]

Investig. Ophthalmol. Vis. Sci. (1)

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Investig. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

J. Biomed. Opt. (1)

S. Chitchian, M. A. Mayer, A. R. Boretsky, F. J. van Kuijk, and M. Motamedi, “Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform,” J. Biomed. Opt. 17, 116009 (2012).
[Crossref] [PubMed]

J. biomedical optics (1)

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration–method and clinical examples,” J. biomedical optics 12, 041208 (2012).
[Crossref]

J. Opt. Soc. Am. A, Opt. image science, vision (1)

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A, Opt. image science, vision 24, 1901–1910 (2007).
[Crossref]

Ophthalmology (4)

S. G. Schuman, A. F. Koreishi, S. Farsiu, S. ho Jung, J. A. Izatt, and C. A. Toth, “Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography,” Ophthalmology 116, 488–496 (2009).
[Crossref] [PubMed]

V. J. Srinivasan, M. Wojtkowski, A. J. Witkin, J. S. Duker, T. H. Ko, M. Carvalho, J. S. Schuman, A. Kowalczyk, and J. G. Fujimoto, “High-definition and 3-dimensional imaging of macular pathologies with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmology 113, 2054–2065 (2006).
[Crossref] [PubMed]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121, 162–172 (2014).
[Crossref]

A. S. Reis, G. P. Sharpe, H. Yang, M. T. Nicolela, C. F. Burgoyne, and B. C. Chauhan, “Optic disc margin anatomy in patients with glaucoma and normal controls with spectral domain optical coherence tomography,” Ophthalmology 119, 738–747 (2012).
[Crossref] [PubMed]

Opt. Express (1)

Opt. Lett. (3)

Other (12)

J. Duan, C. Tench, I. Gottlob, F. Proudlock, and L. Bai, “Optical coherence tomography image segmentation,” in International Conference on Image Processing (ICIP), (IEEE, 2015), pp. 4278–4282.

J. D. Gibbons and S. Chakraborti, Nonparametric Atatistical Inference (Chapman & Hall/Taylor & Francis, 2003).

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” \International Conference on Learning Representations (2014).

I. W. Selesnick, “The double-density dual-tree DWT,” IEEE Transactions on Signal Processing, 52 (2004), pp. 1304–1314.
[Crossref]

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein GANs,” in Advances In Neural Information Processing Systems, (2017), pp. 5767–5777.

D. Perdios, A. Besson, M. Arditi, and J.-P. Thiran, “A deep learning approach to ultrasound image recovery,” in 2017 IEEE International Ultrasonics Symposium (IUS), (IEEE, 2017), pp. 1–4.

L. Gondara, “Medical image denoising using convolutional denoising autoencoders,” in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), (IEEE, 2016), pp. 241–246.
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision (Springer, Cham, 2016), pp. 630–645.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of The 34th International Conference on Machine Learning, (2017), pp. 1–32.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,(2017), pp. 4681–4690.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, (2014).

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in International Society for Optical Engineering the International Society for Optical Engineering, vol. 8669S. Ourselin and D. R. Haynor, eds. (International Society for Optics and Photonics, 2013), pp. 1–7.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (7)

Fig. 1
Fig. 1 Illustration of the proposed networks. The input (IR) to the generator network (a) is a raw B-scan from the OCT scanner, which undergoes processing by B residual blocks (b) to produce an enhanced image (IE). The numbers of filters for the convolutional layers, and the number of units for the dense layers, are indicated by numbers on the blocks. The discriminator network (c, described in 2.3) aims to estimate the Wasserstein metric between real (IFA) and generated (IE) data distributions.
Fig. 2
Fig. 2 OCT Image from a healthy volume captured by Cirrus HD-OCT Scanner (a), and corresponding 6-frame averaged image (b). The result of post-processing of (a) with CNN-MSE (c), CNN-WGAN (d), BM3D (e), and DD-CDWT (f). Three zoomed in, colour coded sections are shown below each B-scan (best viewed in colour).
Fig. 3
Fig. 3 OCT Image from a glaucomatous volume captured by Cirrus HD-OCT Scanner (a), and corresponding 6-frame averaged image (b). The result of post-processing of (a) with CNN-MSE (c), CNN-WGAN (d), BM3D (e), and DD-CDWT (f). Three zoomed in, colour coded sections are shown below each B-scan (best viewed in colour).
Fig. 4
Fig. 4 a) Portion of a B-scan with surface annotations for five layers. Intra-observer (b) and inter-observer (c) annotation location difference (AAD), averaged over the columns of all annotated B-scans for ILM and GCIPL surfaces (mean ± standard error). Statistical significance at p < 0.001 indicated by *. Results for the remaining layers are shown in Figure 6.
Fig. 5
Fig. 5 Qualitative results for perceived clarity for 55 B-scans ranked by observer 1 (a), observer 2 (b) and observer 3 (c) for images acquired by 6-frame averaging (“Ground Truth”), CNN-WGAN, CNN-MSE, BM3D, DD-CDWT and the respective un-processed images (“Raw”). Colour indicates the percentage of images, for the respective image type, in each rank position (where 1=highest rank, 6=lowest rank). Results for accuracy and personal preference are shown in Figure 7.
Fig. 6
Fig. 6 Intra-observer (a) and inter-observer (b) annotation location difference (AAD), averaged over the columns of all annotated B-scans for BM, INL and RNFL surfaces (mean ± standard error). Statistical significance at p < 0.001 indicated by *.
Fig. 7
Fig. 7 Qualitative results for perceived accuracy (a–c) and personal preference (d–f) for 55 B-scans ranked by observer 1 (a, d), observer 2 (b, e) and observer 3 (c, f) for images acquired by 6-frame averaging (“Ground Truth”), CNN-WGAN, CNN-MSE, BM3D, DD-CDWT and the respective un-processed images (“Raw”). Colour indicates the percentage of images, for the respective image type, in each rank position (where 1=highest rank, 6=lowest rank).

Tables (3)

Tables Icon

Table 1 Time required to process a single B-scan, averaged over 200 B-scans.

Tables Icon

Table 2 Mean ± standard deviation of the peak signal to noise ratio(PSNR), structural similarity ratio (SSIM), multi-scale structural similarity ratio (MS-SSIM) and mean squared error (MSE) for 1980 healthy B-scans and 1080 B-scans from patients with glaucoma, using the BM3D, DD-CDWT, and the proposed CNN-WGAN and CNN-MSE networks. The best results are shown in bold. All pairwise comparisons (excluding SSIM on glaucoma images processed by BM3D and DD-CDWT) were statistically significant (p < 0.0001).

Tables Icon

Table 3 Inter-observer agreement as measured by Cohen’s kappa score between the three experts (labelled 1–3) for each of the qualitative metrics.

Equations (7)

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

G : I R I F A .
L VGG / i . j = 1 W i , j H i , j x = 1 W i , j y = 1 H i , j ( ϕ i , j ( I F A ) x , y ϕ i , j ( G θ G ( I R ) ) x , y ) 2
min θ G max θ D L WGAN ( D , G ) = 𝔼 I F A [ D ( I F A ) ] + 𝔼 I R [ D ( G ( I R ) ) ] + λ 𝔼 I F A ^ [ ( Δ I F A ^ D ( I F A ^ ) 2 1 ) 2 ] ,
min θ G max θ D λ 1 L WGAN ( D , G ) + λ 2 L V G G ( G ) + L M S E ( G ) ,
I ˜ = I μ T σ T ,
I = ( I ˜ × σ T ) + μ T .
AAD = 1 N × A n = 1 N a = 1 A | l 1 n , a l 2 n , a |

Metrics