Abstract
Quality of retinal image plays an essential role in ophthalmic disease diagnosis. However, most of the existing models neglect the potential correlation between retinal structure segmentation and retinal image quality assessment (RIQA), since the segmentation result is able to provide the region of interests (ROIs) and the RIQA model can extract more discriminative features. Therefore, in this paper, we incorporate the retinal structure segmentation process into RIQA tasks and thus propose a structure-guided deep neural network (SG-Net) for better image quality assessment. The SG-Net consists of a vessel segmentation module, an optic disc segmentation module, and a quality assessment module. The vessel segmentation module and optic disk segmentation module generate the segmentation results of important retinal structures (i.e., vessel and optic disc) that provide supplementary knowledge to support the quality assessment module. The quality assessment module is a three-branches classification network to extract and fuse features to estimate image quality. We evaluated our proposed SG-Net on the Eye-Quality (EyeQ) database, and the experiment results demonstrated that the proposed SG-Net outperforms other existing state-of-the-art methods. Our ablation studies also indicated that each structure segmentation module is able to achieve impressive performance gain on the EyeQ database.
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References
https://www.kaggle.com/c/diabetic-retinopathy-detection/leaderboard
Abdel-Hamid, L.S., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: No-reference wavelet based retinal image quality assessment. In: Computational Vision and Medical Image Processing V: VIPImage, pp. 123–129 (2015)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708, July 2017
Köhler, T., Budai, A., Kraus, M.F., Odstrčilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95–100 (2013)
Mahapatra, D., Roy, P.K., Sedai, S., Garnavi, R.: Retinal image quality classification using saliency maps and CNNs. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 172–179. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47157-0_21
Patton, N., et al.: Retinal image analysis: concepts, applications and potential. Prog. Retinal Eye Res. 25(1), 99–127 (2006)
Pérez, A.D., Perdomo, O., González, F.A.: A lightweight deep learning model for mobile eye fundus image quality assessment. In: 15th International Symposium on Medical Information Processing and Analysis, vol. 11330, pp. 151–158 (2020)
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
Salazar-Gonzalez, A., Kaba, D., Li, Y., Liu, X.: Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inform. 18(6), 1874–1886 (2014)
Setiawan, A.W., Mengko, T.R., Santoso, O.S., Suksmono, A.B.: Color retinal image enhancement using CLAHE. In: International Conference on ICT for Smart Society, pp. 1–3 (2013)
Shen, Y., et al.: Multi-task fundus image quality assessment via transfer learning and landmarks detection. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 28–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_4
Shen, Y., Sheng, B., Fang, R., Li, H., Dai, L., Stolte, S., Qin, J., Jia, W., Shen, D.: Domain-invariant interpretable fundus image quality assessment. Med. Image Anal. 61, 101654 (2020)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Wang, S., Jin, K., Lu, H., Cheng, C., Ye, J., Qian, D.: Human visual system-based fundus image quality assessment of portable fundus camera photographs. IEEE Trans. Med. Imaging 35(4), 1046–1055 (2016)
Welikala, R., et al.: Automated retinal image quality assessment on the UK biobank dataset for epidemiological studies. Comput. Biol. Med. 71, 67–76 (2016)
Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: Multiscale network followed network model for retinal vessel segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 119–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_14
Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: NFN+: a novel network followed network for retinal vessel segmentation. Neural Netw. 126, 153–162 (2020)
Zago, G.T., Andreão, R.V., Dorizzi, B., Salles, E.O.T.: Retinal image quality assessment using deep learning. Comput. Biol. Med. 103, 64–70 (2018)
Zhang, Z., et al.: ORIGA-light: an online retinal fundus image database for glaucoma analysis and research. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 3065–3068 (2010)
Acknowledgment
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, and in part by the National Natural Science Foundation of China under Grants 61771397. We appreciate the efforts devoted to collect and share the DRIVE, ORIGA-650, and EyeQ datasets for comparing retinal image analysis algorithms.
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Zhou, X., Wu, Y., Xia, Y. (2020). Retinal Image Quality Assessment via Specific Structures Segmentation. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_6
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DOI: https://doi.org/10.1007/978-3-030-63419-3_6
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