Abstract
Retinal vessel segmentation is an important step in clinical analysis of fundus images. Low contrast and the imbalanced pixel ratios between thick and thin vessels make accurate segmentation of the thin vasculature extremely challenging. In this paper, we present a novel multiscale segmentation method named Multiple discriminator generative adversarial network (MuGAN). MuGAN contains multiple discriminators with different effective receptive fields, which are sensitive to features at different scales. These discriminators jointly teach the segmentation (generator) network to pay attention to multiscale patterns. In addition, multiple discriminators allow our model to incorporate multiple inputs, such as edge enhanced vessel images, during training. We evaluated our method on the publicly available DRIVE and STARE datasets. MuGAN achieved an overall area under the Receiver Operator Characteristic Curve (AUC) of 0.979 for DRIVE and 0.981 for the STARE dataset. On segmenting thin retinal vessels, MuGAN showed quantitative and qualitative improvements on baselines.
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References
Poplin, R., et al.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018)
Klein, R., et al.: The relation of retinal vessel caliber to the incidence and progression of diabetic retinopathy: XIX: the Wisconsin epidemiologic study of diabetic retinopathy. Arch. Ophthalmol. 122, 76–83 (2004)
Wang, J.J., et al.: Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations. Eur. Heart J. 28, 1984–1992 (2007)
Yan, Z., Yang, X., Cheng, K.T.: A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inform. 23(4) (2019). https://ieeexplore.ieee.org/document/8476171
Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv:1406.2661 (2014)
Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic Segmentation using Adversarial Networks. arXiv:1611.08408 (2016)
Son, J., Park, S.J., Jung, K.-H.: Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks. arXiv:1706.09318 (2017)
Durugkar, I., Gemp, I., Mahadevan, S.: Generative Multi-Adversarial Networks. arXiv:1611.01673 (2016)
Yu, F., Koltun, V.: Multi-Scale Context Aggregation by Dilated Convolutions. arXiv:1511.07122 (2015)
Yan, Z., Yang, X., Cheng, K.: A skeletal similarity metric for quality evaluation of retinal vessel segmentation. IEEE Trans. Med. Imaging 37, 1045–1057 (2018)
Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_16
Laibacher, T., Weyde, T., Jalali, S.: M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments. arXiv:1811.07738 (2018)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken, B.V.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)
Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19, 203–210 (2000)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
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Tjio, G., Li, S., Xu, X., Ting, D.S.W., Liu, Y., Goh, R.S.M. (2019). Multi-discriminator Generative Adversarial Networks for Improved Thin Retinal Vessel Segmentation. In: Fu, H., Garvin, M., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2019. Lecture Notes in Computer Science(), vol 11855. Springer, Cham. https://doi.org/10.1007/978-3-030-32956-3_18
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DOI: https://doi.org/10.1007/978-3-030-32956-3_18
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