Abstract

Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers’ eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]
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    [Crossref]
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    [Crossref]
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2019 (6)

Z. Yu, Q. Xiang, J. Meng, C. Kou, Q. Ren, and Y. Lu, “Retinal image synthesis from multiple-landmarks input with generative adversarial networks,” Biomed. Eng. Online 18(1), 62 (2019).
[Crossref]

Y. Lu, M. Kowarschik, X. Huang, Y. Xia, J.-H. Choi, S. Chen, S. Hu, Q. Ren, R. Fahrig, J. Hornegger, and A. Maier, “A learning-based material decomposition pipeline for multi-energy x-ray imaging,” Med. Phys. 46(2), 689–703 (2019).
[Crossref]

X. Liu, Z. Huang, Z. Wang, C. Wen, Z. Jiang, Z. Yu, J. Liu, G. Liu, X. Huang, A. Maier, Q. Ren, and Y. Lu, “A deep learning based pipeline for optical coherence tomography angiography,” J. Biophotonics 12(10), e201900008 (2019).
[Crossref]

A. Maier, C. Syben, T. Lasser, and C. Riess, “A gentle introduction to deep learning in medical image processing,” Z. Med. Phys. 29(2), 86–101 (2019).
[Crossref]

F. Shi, N. Cai, Y. Gu, D. Hu, Y. Ma, Y. Chen, and X. Chen, “DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images,” Phys. Med. Biol. 64(17), 175010 (2019).
[Crossref]

W. Jifara, F. Jiang, S. Rho, M. Cheng, and S. Liu, “Medical image denoising using convolutional neural network: a residual learning approach,” J. Supercomput. 75(2), 704–718 (2019).
[Crossref]

2018 (10)

X. Wang, X. Yu, X. Liu, C. Si, S. Chen, N. Wang, and L. Liu, “A two-step iteration mechanism for speckle reduction in optical coherence tomography,” Biomed. Signal Process. & Control. 43, 86–95 (2018).
[Crossref]

C. You, Q. Yang, L. Gjesteby, G. Li, S. Ju, Z. Zhang, Z. Zhao, Y. Zhang, W. Cong, and G. Wang, “Structurally-sensitive multi-scale deep neural network for low-dose CT denoising,” IEEE Access 6, 41839–41855 (2018).
[Crossref]

Y. Lu, M. Kowarschik, X. Huang, S. Chen, Q. Ren, R. Fahrig, J. Hornegger, and A. Maier, “Material decomposition using ensemble learning for spectral x-ray imaging,” IEEE Trans. Radiat. Plasma Med. Sci. 2(3), 194–204 (2018).
[Crossref]

Z. Jiang, Z. Yu, S. Feng, Z. Huang, Y. Peng, J. Guo, Q. Ren, and Y. Lu, “A super-resolution method-based pipeline for fundus fluorescein angiography imaging,” Biomed. Eng. Online 17(1), 125 (2018).
[Crossref]

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (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]

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. on Image Process. 27(12), 5880–5891 (2018).
[Crossref]

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, and N. G. Strouthidis, “Drunet: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
[Crossref]

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref]

2017 (4)

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (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]

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
[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,” IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017).
[Crossref]

2016 (1)

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref]

2015 (1)

2013 (2)

B. Chong and Y.-K. Zhu, “Speckle reduction in optical coherence tomography images of human finger skin by wavelet modified BM3D filter,” Opt. Commun. 291, 461–469 (2013).
[Crossref]

W. Wu, O. Tan, R. R. Pappuru, H. Duan, and D. Huang, “Assessment of frame-averaging algorithms in OCT image analysis,” Ophthalmic Surgery, Lasers Imaging Retin. 44(2), 168–175 (2013).
[Crossref]

2011 (1)

D. Alonso-Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16(11), 116027 (2011).
[Crossref]

2009 (1)

Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26(1), 98–117 (2009).
[Crossref]

2007 (1)

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. A. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples,” J. Biomed. Opt. 12(4), 041208 (2007).
[Crossref]

2006 (1)

T. Kume, T. Akasaka, T. Kawamoto, N. Watanabe, E. Toyota, Y. Neishi, R. Sukmawan, Y. Sadahira, and K. Yoshida, “Assessment of coronary arterial plaque by optical coherence tomography,” Am. J. Cardiol. 97(8), 1172–1175 (2006).
[Crossref]

2005 (3)

J. Schmitt, D. Kolstad, and C. Petersen, “Intravascular Optical Coherence Tomography—Opening a Window into Coronary Artery Disease,” Light. Imaging, Inc. Bus. Briefing: Eur. Cardiol. 1(1), 1–5 (2005).
[Crossref]

P. H. Tomlins and R. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
[Crossref]

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40(2), 85–94 (2005).
[Crossref]

2004 (2)

M. C. Pierce, J. Strasswimmer, B. H. Park, B. Cense, and J. F. de Boer, “Advances in optical coherence tomography imaging for dermatology,” J. Invest. Dermatol. 123(3), 458–463 (2004).
[Crossref]

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).
[Crossref]

2003 (1)

J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nat. Biotechnol. 21(11), 1361–1367 (2003).
[Crossref]

2001 (1)

W. Drexler, U. Morgner, R. K. Ghanta, F. X. Kärtner, J. S. Schuman, and J. G. Fujimoto, “Ultrahigh-resolution ophthalmic optical coherence tomography,” Nat. Med. 7(4), 502–507 (2001).
[Crossref]

1999 (1)

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

1995 (1)

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref]

Akasaka, T.

T. Kume, T. Akasaka, T. Kawamoto, N. Watanabe, E. Toyota, Y. Neishi, R. Sukmawan, Y. Sadahira, and K. Yoshida, “Assessment of coronary arterial plaque by optical coherence tomography,” Am. J. Cardiol. 97(8), 1172–1175 (2006).
[Crossref]

Alonso-Caneiro, D.

D. Alonso-Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16(11), 116027 (2011).
[Crossref]

Altmeyer, P.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40(2), 85–94 (2005).
[Crossref]

Antony, B. J.

Aum, J.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Proc. 3rd Int. Conf. on Learn. Represent. (2014).

Bogunovic, H.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref]

Bovik, A. C.

Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26(1), 98–117 (2009).
[Crossref]

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).
[Crossref]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2 (IEEE, 2003), pp. 1398–1402.

Cai, N.

F. Shi, N. Cai, Y. Gu, D. Hu, Y. Ma, Y. Chen, and X. Chen, “DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images,” Phys. Med. Biol. 64(17), 175010 (2019).
[Crossref]

Cense, B.

M. C. Pierce, J. Strasswimmer, B. H. Park, B. Cense, and J. F. de Boer, “Advances in optical coherence tomography imaging for dermatology,” J. Invest. Dermatol. 123(3), 458–463 (2004).
[Crossref]

Chakraborty, D.

Chang, R. T.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref]

Chatterjee, J.

Chen, H.

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. on Image Process. 27(12), 5880–5891 (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,” IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017).
[Crossref]

Chen, S.

Y. Lu, M. Kowarschik, X. Huang, Y. Xia, J.-H. Choi, S. Chen, S. Hu, Q. Ren, R. Fahrig, J. Hornegger, and A. Maier, “A learning-based material decomposition pipeline for multi-energy x-ray imaging,” Med. Phys. 46(2), 689–703 (2019).
[Crossref]

X. Wang, X. Yu, X. Liu, C. Si, S. Chen, N. Wang, and L. Liu, “A two-step iteration mechanism for speckle reduction in optical coherence tomography,” Biomed. Signal Process. & Control. 43, 86–95 (2018).
[Crossref]

Y. Lu, M. Kowarschik, X. Huang, S. Chen, Q. Ren, R. Fahrig, J. Hornegger, and A. Maier, “Material decomposition using ensemble learning for spectral x-ray imaging,” IEEE Trans. Radiat. Plasma Med. Sci. 2(3), 194–204 (2018).
[Crossref]

Chen, X.

F. Shi, N. Cai, Y. Gu, D. Hu, Y. Ma, Y. Chen, and X. Chen, “DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images,” Phys. Med. Biol. 64(17), 175010 (2019).
[Crossref]

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. on Image Process. 27(12), 5880–5891 (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]

Chen, Y.

F. Shi, N. Cai, Y. Gu, D. Hu, Y. Ma, Y. Chen, and X. Chen, “DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images,” Phys. Med. Biol. 64(17), 175010 (2019).
[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]

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,” IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017).
[Crossref]

Cheng, M.

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D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. on Image Process. 27(12), 5880–5891 (2018).
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Z. Yu, Q. Xiang, J. Meng, C. Kou, Q. Ren, and Y. Lu, “Retinal image synthesis from multiple-landmarks input with generative adversarial networks,” Biomed. Eng. Online 18(1), 62 (2019).
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Zhu, W.

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).
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Am. J. Cardiol. (1)

T. Kume, T. Akasaka, T. Kawamoto, N. Watanabe, E. Toyota, Y. Neishi, R. Sukmawan, Y. Sadahira, and K. Yoshida, “Assessment of coronary arterial plaque by optical coherence tomography,” Am. J. Cardiol. 97(8), 1172–1175 (2006).
[Crossref]

Appl. Opt. (1)

Biomed. Eng. Online (2)

Z. Yu, Q. Xiang, J. Meng, C. Kou, Q. Ren, and Y. Lu, “Retinal image synthesis from multiple-landmarks input with generative adversarial networks,” Biomed. Eng. Online 18(1), 62 (2019).
[Crossref]

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[Crossref]

Biomed. Opt. Express (4)

Biomed. Signal Process. & Control. (1)

X. Wang, X. Yu, X. Liu, C. Si, S. Chen, N. Wang, and L. Liu, “A two-step iteration mechanism for speckle reduction in optical coherence tomography,” Biomed. Signal Process. & Control. 43, 86–95 (2018).
[Crossref]

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Figures (7)

Fig. 1.
Fig. 1. Schematic description of the deep learning-based denoising pipeline for OCT images.
Fig. 2.
Fig. 2. Schematic overview of the neural network architecture in this study.
Fig. 3.
Fig. 3. Noisy and label images used in the training phase. (A-C) Noisy OCT images; (D-F) the corresponding label images generated by averaging 50 frames of registered B-scans acquired from a region with scans occurring in one direction.
Fig. 4.
Fig. 4. Noisy OCT images (A-D) and the corresponding denoised OCT images (E-H).
Fig. 5.
Fig. 5. Denoised results of an OCT image processed by different loss functions. (A) original noisy image; (B) $\mathrm {L}_1$ loss function; (C) MSE loss function; (D) combination of edge and $\mathrm {L}_1$; (E) combination of edge and MSE terms; (F) perceptually-sensitive loss function; (G) combination of perceptually-sensitive and $\mathrm {L}_1$; (H) combination of perceptually-sensitive and MSE.
Fig. 6.
Fig. 6. Denoised results of two OCT images using (A, D) BM3D; (B, E) NLM; (C, F) CNN with the perceptually-sensitive loss function.
Fig. 7.
Fig. 7. The PSNR values plotted against different weights $\lambda _{B}$ of the conventional loss functions ($\mathrm {L}_1$ and MSE) in the compound loss function(Eq.12), while $\lambda _{A}$ was fixed to 1.

Tables (4)

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Table 1. Groups of the loss functions

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Table 2. Quantitative evaluation (mean and standard deviation) across different loss functions.

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Table 3. Quantitative evaluation (mean and standard deviation) across BM3D, NLM and CNN with the perceptually-sensitive loss function.

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Table 4. Quantitative evaluation (mean and standard deviation) across three network architectures with the perceptually-sensitive loss function.

Equations (13)

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

N r = S N s + N b ,
S ^ = R ( N r ) ,
arg min Θ L ( R Θ ( N r ) , S l ) ,
S S I M ( S l , S ^ ) = ( 2 u S ^ u S l + C 1 u S ^ 2 + u S l 2 + C 1 × 2 σ S l S ^ + C 2 σ S ^ 2 + σ S l 2 + C 2 ) ,
M S S S I M ( S l , S ^ ) = i = 1 M S S I M ( S i l , S i ) ^ ,
L p e r c e t u a l l y s e n s i t i v e = 1 M S S S I M ( S l , S ^ ) ,
M S E = M , N ( S l S ^ ) 2 M × N ,
P S N R = 10 × l o g 10 ( M A X S l 2 M S E ) ,
L M S E = 1 H × W S l S ^ 2 2 ,
L L 1 = 1 H × W | S l S ^ | ,
L e d g e = E [ l o g i , j | S i + 1 , j l S i , j l | i , j | S ^ i + 1 , j S ^ i , j | ] ,
L C p e r c e p t u a l l y s e n s i t i v e = λ A × L P e r c e p t u a l l y s e n s i t i v e + λ B × L B ,
L C e d g e a w a r e = λ A × L E d g e a w a r e + λ B × L B ,