Abstract
Retinal vessel segmentation based on deep learning requires a lot of manual labeled data. That’s time-consuming, laborious and professional. In this paper, we propose a data-efficient semi-supervised learning framework, which effectively combines the existing deep learning network with generative adversarial networks (GANs) and self-training ideas. In view of the difficulty of tuning hyper-parameters of semi-supervised learning, we propose a method for hyper-parameters selection based on particle swarm optimization (PSO) algorithm. This work is the first demonstration that combines intelligent optimization with semi-supervised learning for achieving the best performance. Under the collaboration of adversarial learning, self-training and PSO, we obtain the performance of retinal vessel segmentation approximate to or even better than representative supervised learning using only one tenth of the labeled data from DRIVE.
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References
Abramoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
Krause, M., Alles, R.M., Burgeth, B., Weickert, J.: Fast retinal vessel analysis. J. Real-Time Image Proc. 11(2), 413–422 (2016)
Fraz, M.M., et al.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Programs Biomed. 108(2), 600–616 (2012)
Wang, Y., Ji, G., Lin, P., Trucco, E.: Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recogn. 46(8), 2117–2133 (2013)
Mapayi, T., Viriri, S., Tapamo, J.-R.: Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput. Math. Methods Med. 2015, 11 (2015)
Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_66
Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)
Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_17
Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic using generative adversarial network. In: IEEE International Conference Computer Vision (ICCV), pp. 5689–5697 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Esmin, A., Coelho, R., Matwin, S.: A review on particle swarm optimization algorithm and its variants to clustering high dimensional data. Artif. Intell. Rev. 44(1), 23–45 (2015)
Lorenzo, P.R., et al.: Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM (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
Staal, J., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)
Soares, J., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)
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Huo, Q., Tang, G., Zhang, F. (2019). Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks. In: Liao, H., et al. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH CVII-STENT 2019 2019. Lecture Notes in Computer Science(), vol 11794. Springer, Cham. https://doi.org/10.1007/978-3-030-33327-0_14
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DOI: https://doi.org/10.1007/978-3-030-33327-0_14
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