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2019 | OriginalPaper | Buchkapitel

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

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

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

Verlag: 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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Literatur
1.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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.
Zurück zum Zitat 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)
Metadaten
Titel
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
verfasst von
Ilja Manakov
Markus Rohm
Christoph Kern
Benedikt Schworm
Karsten Kortuem
Volker Tresp
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_1

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