Abstract
Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. The degradation prevents reliable diagnosis by ophthalmologists or computer-aided systems. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images. Unfortunately, challenges remain in the deployment of these algorithms, such as collecting sufficient training data and preserving retinal structures. In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure. A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. Then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced. The experiments demonstrate the effectiveness of SCR-Net in the comparison with state-of-the-art methods and the follow-up clinical applications. The code is available at https://github.com/liamheng/Annotation-free-Fundus-Image-Enhancement.
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References
Cao, L., Li, H., Zhang, Y.: Retinal image enhancement using low-pass filtering and \(\alpha \)-rooting. Sign. Process. 170, 107445 (2020)
Chen, J., Tan, C.H., Hou, J., Chau, L.P., Li, H.: Robust video content alignment and compensation for rain removal in a cnn framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6286–6295 (2018)
Cheng, J., et al.: Structure-preserving guided retinal image filtering and its application for optic disk analysis. IEEE Trans. Med. Imaging 37(11), 2536–2546 (2018)
Cheng, P., Lin, L., Huang, Y., Lyu, J., Tang, X.: I-SECRET: importance-guided fundus image enhancement via semi-supervised contrastive constraining. In: de Bruijne, M. (ed.) MICCAI 2021. LNCS, vol. 12908, pp. 87–96. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_9
Huang, T., Li, S., Jia, X., Lu, H., Liu, J.: Neighbor2neighbor: Self-supervised denoising from single noisy images. arXiv preprint arXiv:2101.02824 (2021)
Li, H., et al.: An annotation-free restoration network for cataractous fundus images. IEEE Transactions on Medical Imaging (2022)
Li, H., et al.: Restoration of cataract fundus images via unsupervised domain adaptation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 516–520. IEEE (2021)
Li, T., et al.: Applications of deep learning in fundus images: A review. Medical Image Analysis p. 101971 (2021)
Liu, H., et al.: Domain generalization in restoration of cataract fundus images via high-frequency components. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)
Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. 61, 650–662 (2017)
Luo, Y., et al.: Dehaze of cataractous retinal images using an unpaired generative adversarial network. IEEE J. Biomed. Health Inform. 24(1), 3374–3383 (2020)
MacGillivray, T.J., et al.: Suitability of UK biobank retinal images for automatic analysis of morphometric properties of the vasculature. PLoS ONE 10(5), e0127914 (2015)
Mitra, A., Roy, S., Roy, S., Setua, S.K.: Enhancement and restoration of non-uniform illuminated fundus image of retina obtained through thin layer of cataract. Comput. Methods Programs Biomed. 156, 169–178 (2018)
Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319–345. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_19
Peli, E., Peli, T.: Restoration of retinal images obtained through cataracts. IEEE Trans. Med. Imaging 8(4), 401–406 (1989)
Shen, Z., Fu, H., Shen, J., Shao, L.: Modeling and enhancing low-quality retinal fundus images. IEEE Trans. Med. Imaging 40(3), 996–1006 (2020)
Wang, S., Yu, L., Yang, X., Fu, C.W., Heng, P.A.: Patch-based output space adversarial learning for joint optic disc and cup segmentation. IEEE Trans. Med. Imaging 38(11), 2485–2495 (2019)
Zhang, W., Zhong, J., Yang, S., Gao, Z., Hu, J., Chen, Y., Yi, Z.: Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl.-Based Syst. 175, 12–25 (2019)
Zhang, X., Hu, Y., Xiao, Z., Fang, J., Higashita, R., Liu, J.: Machine learning for cataract classification/grading on ophthalmic imaging modalities: a survey. Mach. Intell. Res. 19, 184–208 (2022)
Zhang, X., et al.: Adaptive feature squeeze network for nuclear cataract classification in as-oct image. J. Biomed. Inform. 128, 104037 (2022)
Zhao, R., Chen, X., Liu, X., Chen, Z., Guo, F., Li, S.: Direct cup-to-disc ratio estimation for glaucoma screening via semi-supervised learning. IEEE J. Biomed. Health Inform. 24(4), 1104–1113 (2019)
Zhu, J.Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2223–2232 (2017)
Acknowledgment
This work was supported in part by Basic and Applied Fundamental Research Foundation of Guangdong Province (2020A1515110286), The National Natural Science Foundation of China (8210072776), Guangdong Provincial Department of Education (2020ZDZX3043), Guangdong Provincial Key Laboratory (2020B121201001), Shenzhen Natural Science Fund (JCYJ20200109140820699, 20200925174052004), and A*STAR AME Programmatic Fund (A20H4b0141).
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Li, H. et al. (2022). Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement. 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_47
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