Abstract
Optical coherence tomography (OCT) is a common imaging examination in ophthalmology, which can visualize cross-sectional retinal structures for diagnosis. However, image quality still suffers from speckle noise and other motion artifacts. An effective OCT denoising method is needed to ensure the image is interpreted correctly. However, lack of paired clean image restricts its development. Here, we propose an end-to-end structure-aware noise reduction generative adversarial network (SNR-GAN), trained with un-paired OCT images. The network is designed to translate images between noisy domain and clean domain. Besides adversarial and cycle consistence loss, structure-aware loss based on structural similarity index (SSIM) is added to the objective function, so as to achieve more structural constraints during image denoising. We evaluated our method on normal and pathological OCT datasets. Compared to the traditional methods, our proposed method achieved the best denoising performance and subtle structural preservation.
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References
Adhi, N., Duker, J.S.: Optical coherence tomography-current and future applications. Curr. Opin. Ophthalmol. 24, 213–221 (2013)
Salinas, H.M., Fernández, D.C.: Comparison of PDE-Based nonlinear diffusion ap-proaches for image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imaging 26(6), 761–771 (2007)
Mayer, M.A., Borsdorf, A., Wagner, M., et al.: Wavelet denoising of multiframe optical coherence tomography data. Biomed. Opt. Express 3(3), 572–589 (2012)
Aum, J., Kim, J.H., Jeong, J.: Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian aniso-tropic kernels. Appl. Opt. 54(13), D43–D50 (2015)
Chong, B., Zhu, Y.: Speckle reduction in optical coherence tomography images of human finger skin by wavelet modified BM3D filter. Opt. Commun. 291, 461–469 (2013)
Li, M., Idoughi, R., Choudhury, B., et al.: Statistical model for OCT image denoising. Biomed. Opt. Express 8(9), 3903–3917 (2017)
Devalla, S.K., Subramanian, G., Pham, T.H., et al.: A deep learning approach to denoise optical coherence tomography images of the optic nerve head. arXiv preprint arXiv:1809.10589 (2018)
Ma, Y., Chen, X., Zhu, W., et al.: Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN. Biomed. Opt. Express 9(11), 5129–5146 (2018)
Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. In: Computer Vision and Pattern Recognition (2017)
Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International Conference on Computer Vision (2017)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. In: Advances in Neural Information Processing Systems, Montreal (2014)
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Kafieh, R., Rabbani, H., Selesnick, I.: Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imag. 34(5), 1042–1062 (2015)
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Guo, Y. et al. (2019). Structure-Aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image. In: Fu, H., Garvin, M., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2019. Lecture Notes in Computer Science(), vol 11855. Springer, Cham. https://doi.org/10.1007/978-3-030-32956-3_2
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DOI: https://doi.org/10.1007/978-3-030-32956-3_2
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