Abstract
Different patterns of retinal arterioles and venules in the fundus images form an important metric to measure the disease severity. Therefore, an accurate classification of arterioles and venules is greatly necessary. In this work, we propose a novel network, named as the dual Deeply-Supervised Network (dual DSN), to classify arterioles and venules on retinal fundus images. We employ the U-shape network (U-Net) as the backbone of our proposed model. Our proposed dual DSN produces an auxiliary output of the network at every scale, which generates a loss by comparing to the manual annotation. The losses in the encoding path of dual DSN regularize the low-level features, while those in the decoding path of dual DSN regularize the high-level features. In sum, such losses in dual DSN form dual supervision to the backbone U-Net and capture the multi-level features of the input image, which improves the classification of retinal arterioles and venules. The experimental results demonstrate that the proposed dual DSN outperforms the previous state-of-the-art methods on DRIVE dataset with an accuracy of 95.0%.
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References
AlBadawi, S., Fraz, M.M.: Arterioles and venules classification in retinal images using fully convolutional deep neural network. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 659–668. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_75
Estrada, R., Allingham, M.J., Mettu, P.S., Cousins, S.W., Tomasi, C., Farsiu, S.: Retinal artery-vein classification via topology estimation. IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015)
Hu, Q., Abrà moff, M.D., Garvin, M.K.: Automated separation of binary overlapping trees in low-contrast color retinal images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 436–443. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_54
Kondermann, C., Kondermann, D., Yan, M.: Blood vessel classification into arteries and veins in retinal images. In: Medical Imaging 2007: Image Processing, vol. 6512, p. 651247. International Society for Optics and Photonics (2007)
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
Shuangling, W., Yilong, Y., Guibao, C., Benzheng, W., Yuanjie, Z., Gongping, Y.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149, 708–717 (2015)
Staal, J., Abrà moff, 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)
Welikala, R., et al.: Automated arteriole and venule classification using deep learning for retinal images from the UK biobank cohort. Comput. Biol. Med. 90, 23–32 (2017)
Xu, X., Ding, W., Abrà moff, M.D., Cao, R.: An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image. Comput. Methods Programs Biomed. 141, 3–9 (2017)
Xu, X., et al.: Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics. Sci. Rep. 6, 34603 (2016)
Xu, X., et al.: Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database. Biomed. Opt. Express 9(7), 3153–3166 (2018)
Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P., Duits, R., ter Haar Romeny, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
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Li, M. et al. (2020). Automated Classification of Arterioles and Venules for Retina Fundus Images Using Dual Deeply-Supervised Network. In: Li, Q., Leahy, R., Dong, B., Li, X. (eds) Multiscale Multimodal Medical Imaging. MMMI 2019. Lecture Notes in Computer Science(), vol 11977. Springer, Cham. https://doi.org/10.1007/978-3-030-37969-8_8
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DOI: https://doi.org/10.1007/978-3-030-37969-8_8
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