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2019 | OriginalPaper | Chapter

Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

Authors: Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, Volker Tresp

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model’s feature maps reveals that it has learned to distinguish retinal layers and other distinct regions of the images.
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Literature
1.
go back to reference Chang, S.G., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000) MathSciNetCrossRef Chang, S.G., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000) MathSciNetCrossRef
2.
go back to reference Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering, vol. 6064 (2006) Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering, vol. 6064 (2006)
3.
go back to reference Darbon, J., Cunha, A., Chan, T.F., Osher, S., Jensen, G.J.: Fast nonlocal filtering applied to electron cryomicroscopy. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1331–1334 (2008) Darbon, J., Cunha, A., Chan, T.F., Osher, S., Jensen, G.J.: Fast nonlocal filtering applied to electron cryomicroscopy. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1331–1334 (2008)
4.
go back to reference Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014) Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014)
5.
go back to reference Halupka, K.J., et al.: Retinal optical coherence tomography image enhancement via deep learning. Biomed. Opt. Express 9(12), 6205–6221 (2018) CrossRef Halupka, K.J., et al.: Retinal optical coherence tomography image enhancement via deep learning. Biomed. Opt. Express 9(12), 6205–6221 (2018) CrossRef
6.
go back to reference He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
7.
go back to reference Huang, Y., et al.: Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. Opt. Express 27(9), 12289–12307 (2019) CrossRef Huang, Y., et al.: Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. Opt. Express 27(9), 12289–12307 (2019) CrossRef
8.
go back to reference Joseph, M., Schmitt, S.H., Xiang, K.M.Y.: Speckle in optical coherence tomography. J. Biomed. Opt. 4(1), 95–105 (1999) CrossRef Joseph, M., Schmitt, S.H., Xiang, K.M.Y.: Speckle in optical coherence tomography. J. Biomed. Opt. 4(1), 95–105 (1999) CrossRef
9.
go back to reference Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 163–169 (1987) Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 163–169 (1987)
11.
go back to reference Podoleanu, A.G.: Optical coherence tomography. J. Microsc. 247(3), 209–219 (2012) CrossRef Podoleanu, A.G.: Optical coherence tomography. J. Microsc. 247(3), 209–219 (2012) CrossRef
12.
go back to reference Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996) CrossRef Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996) CrossRef
13.
go back to reference Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846 (1998) Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846 (1998)
14.
go back to reference Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017) Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
Metadata
Title
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
Authors
Ilja Manakov
Markus Rohm
Christoph Kern
Benedikt Schworm
Karsten Kortuem
Volker Tresp
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_1

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