Abstract
Optical Coherence Tomography Angiography (OCTA) is a novel imaging modality that captures the retinal and choroidal microvasculature in a non-invasive way. So far, 3 mm \(\times \) 3 mm and 6 mm \(\times \) 6 mm scanning protocols have been the two most widely-used field-of-views. Nevertheless, since both are acquired with the same number of A-scans, resolution of 6 mm \(\times \) 6 mm image is inadequately sampled, compared with 3 mm \(\times \) 3 mm. Moreover, conventional supervised super-resolution methods for OCTA images are trained with pixel-wise registered data, while clinical data is mostly unpaired. This paper proposes an inverse-consistent generative adversarial network (GAN) for archiving 6 mm \(\times \) 6 mm OCTA images with super-resolution. Our method is designed to be trained with unpaired 3 mm \(\times \) 3 mm and 6 mm \(\times \) 6 mm OCTA image datasets. To further enhance the super-resolution performance, we introduce frequency transformations to refine high-frequency information while retaining low-frequency information. Compared with other state-of-the-art methods, our approach outperforms them on various performance metrics.
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References
de Carlo, T.E., Salz, D.A., Waheed, N.K., Baumal, C.R., Duker, J.S., Witkin, A.J.: Visualization of the retinal vasculature using wide-field montage optical coherence tomography angiography. Ophthal. Surg. Lasers Imaging Retina 46(6), 611 (2015)
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Synergistic image and feature adaptation: towards cross-modality domain adaptation for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 865–872 (2019)
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans. Med. Imaging 39(7), 2494–2505 (2020)
Cheung, C.M.G., et al.: Diabetic macular ischaemia-a new therapeutic target? Prog. Retinal Eye Res. 101033 (2021)
Cotter, F.: Uses of Complex Wavelets in Deep Convolutional Neural Networks. Ph.D. thesis, University of Cambridge (2020)
Fritsche, M., Gu, S., Timofte, R.: Frequency separation for real-world super-resolution. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3599–3608. IEEE (2019)
Gao, M., Guo, Y., Hormel, T., Sun, J., Hwang, T., Jia, Y.: Reconstruction of high-resolution 6\(\times \)6-mm oct angiograms using deep learning. Biomed. Opt. Exp. 11, 3585–3600 (2020). https://doi.org/10.1364/BOE.394301
Gao, M., et al.: An open-source deep learning network for reconstruction of high-resolution oct angiograms of retinal intermediate and deep capillary plexuses. Investigat. Ophthalmol. Vis. Sci. 62, 1032–1032 (2021). https://doi.org/10.1167/tvst.10.13.13
Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)
Hwang, T.S., et al.: Optical coherence tomography angiography features of diabetic retinopathy. Retina 35(11), 2371 (2015)
Jia, Y., et al.: Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye. Proc. Natl. Acad. Sci. 112(18), E2395–E2402 (2015)
Jia, Y., et al.: Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration. Ophthalmology 121(7), 1435–1444 (2014)
Kim, G., et al.: Unsupervised real-world super resolution with cycle generative adversarial network and domain discriminator. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 456–457 (2020)
Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3408–3416. IEEE (2019)
Maeda, S.: Unpaired image super-resolution using pseudo-supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 291–300 (2020)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Roisman, L., et al.: Optical coherence tomography angiography of asymptomatic neovascularization in intermediate age-related macular degeneration. Ophthalmology 123(6), 1309–1319 (2016)
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
Rosen, R.B., et al.: Earliest evidence of preclinical diabetic retinopathy revealed using optical coherence tomography angiography perfused capillary density. Am. J. Ophthalmol. 203, 103–115 (2019)
Sun, Z.,et al.: Oct angiography metrics predict progression of diabetic retinopathy and development of diabetic macular edema: a prospective study. Ophthalmology 126(12), 1675–1684 (2019)
Tang, F.Y., et al.: Determinants of quantitative optical coherence tomography angiography metrics in patients with diabetes. Sci. Rep. 7(1), 1–10 (2017)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wei, Y., Gu, S., Li, Y., Timofte, R., Jin, L., Song, H.: Unsupervised real-world image super resolution via domain-distance aware training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13385–13394 (2021)
Wong, T.Y., Cheung, C.M.G., Larsen, M., Sharma, S., Simó, R.: Diabetic retinopathy. Nat. Rev. Dis. Primers 2(1), 16012 (2016)
Yang, D.W., et al.: Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an octa study. Br. J. Ophthalmol. (2022). https://doi.org/10.1136/bjophthalmol-2021-320779
Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., Lin, L.: Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 701–710 (2018)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Computer Vision (ICCV), 2017 IEEE International Conference on (2017)
Acknowledgments
This work was supported by funding from Center for Aging Science, Hong Kong University of Science and Technology, and Shenzhen Science and Technology Innovation Committee (Project No. SGDX20210823103201011), and Direct Grants from The Chinese University of Hong Kong (Project Code: 4054419 & 4054487).
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Zhang, W., Yang, D., Cheung, C.Y., Chen, H. (2022). Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_62
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