Abstract
Noise in images presents a considerable problem, limiting their readability and hindering the performance of post-processing and analysis tools. In particular, optical coherence tomography angiography (OCTA) suffers from stripe noise. In medical imaging, clinicians rely on high quality images in order to make accurate diagnoses and plan management. Poor quality images can lead to pathology being overlooked or undiagnosed. Image denoising is a fundamental technique that can be developed to tackle this problem and improve performance in many applications, yet there exists no method focused on removing stripe noise in OCTA. Existing OCTA denoising methods do not consider the structure of stripe noise, which severely limits their potential for recovering the image. The development of artificial intelligence (AI) have enabled deep learning approaches to obtain impressive results and play a dominant role in many areas, but require a ground truth for training, which is difficult to obtain for this problem. In this paper, we propose a revised U-net framework for removing the stripe noise from OCTA images, leaving a clean image. With our proposed method, a ground truth is not required for training, allowing both the stripe noise and the clean image to be estimated, preserving more image detail without compromising image quality. The experimental results show the impressive de-striping performance of our method on OCTA images. We evaluate the effectiveness of our proposed method using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), achieving excellent results as well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. (2017). https://doi.org/10.1001/jamaophthalmol.2017.3782
Chang, Y., Yan, L., Wu, T., Zhong, S.: Remote sensing image stripe noise removal: from image decomposition perspective. IEEE Trans. Geosci. Remote Sens. 54(12), 7018–7031 (2016). https://doi.org/10.1109/TGRS.2016.2594080
De Fauw, J., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342 (2018)
Divakar, N., Babu, R.V.: Image denoising via CNNs: an adversarial approach. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017, pp. 1076–1083, July 2017. https://doi.org/10.1109/CVPRW.2017.145
Gibson, E., et al.: Niftynet: a deep-learning platform for medical imaging. Comput. Methods Programs Biomed. 158, 113–122 (2018)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA - J. Am. Med. Assoc. (2016). https://doi.org/10.1001/jama.2016.17216
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE, August 2010. http://ieeexplore.ieee.org/document/5596999/
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Litjens, G., et al.: A survey on deep learning in medical image analysis (2017). https://doi.org/10.1016/j.media.2017.07.005
Liu, P., Fang, R.: Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising. CoRR arXiv abs/1707.0(c), pp. 1–11, July 2017. http://arxiv.org/abs/1707.05414arxiv.org/abs/1707.09135
Liu, P., Fang, R.: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior. ArXiv preprint, pp. 1–16, July 2017. http://arxiv.org/abs/1707.05414
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE, 07–12 June 2015. http://ieeexplore.ieee.org/document/7298965/, https://doi.org/10.1109/CVPR.2015.7298965
Munk, M.R., et al.: OCT-angiography: a qualitative and quantitative comparison. PLoS ONE (2017). https://doi.org/10.1371/journal.pone.0177059
Nam, S., Hwang, Y., Matsushita, Y., Kim, S.J.: A holistic approach to cross-channel image noise modeling and its application to image denoising. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1683–1691. IEEE, June 2016. http://ieeexplore.ieee.org/document/7780555/, https://doi.org/10.1109/CVPR.2016.186
Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586–1595 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ting, D.S., Liu, Y., Burlina, P., Xu, X., Bressler, N.M., Wong, T.Y.: AI for medical imaging goes deep. Nat. Med. (2018). https://doi.org/10.1038/s41591-018-0029-3
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wikipedia: Total variation denoising (2019)
Yair, N., Michaeli, T.: Multi-scale weighted nuclear norm image restoration. In: CVPR, pp. 3165–3174 (2018). https://doi.org/10.1109/CVPR.2018.00334
Zhang, K., Zuo, W., Member, S., Chen, Y., Meng, D.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017). https://www4.comp.polyu.edu.hk/~cslzhang/paper/DnCNN.pdf
Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss Functions for Image Restoration with Neural Networks. Arxiv XX(X), 1–11 (2015). http://arxiv.org/abs/1511.08861, https://doi.org/10.1109/TCI.2016.2644865
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, D., Celik, N., Wu, X., Williams, B.M., Stylianides, A., Zheng, Y. (2020). A Novel Deep Learning Based OCTA De-striping Method. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-39343-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39342-7
Online ISBN: 978-3-030-39343-4
eBook Packages: Computer ScienceComputer Science (R0)