Abstract
Glaucoma is a highly threatening and widespread ocular disease which may lead to permanent loss in vision. One of the important parameters used for Glaucoma screening in the cup-to-disc ratio (CDR), which requires accurate segmentation of optic cup and disc. We explore fully convolutional networks (FCNs) for the task of joint segmentation of optic cup and disc. We propose a novel improved architecture building upon FCNs by using the concept of residual learning. Additionally, we also explore if adversarial training helps in improving the segmentation results. The method does not require any complicated preprocessing techniques for feature enhancement. We learn a mapping between the retinal images and the corresponding segmentation map using fully convolutional and adversarial networks. We perform extensive experiments of various models on a set of 159 images from RIM-ONE database and also do extensive comparison. The proposed method outperforms the state of the art methods on various evaluation metrics for both disc and cup segmentation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv.org, November 2016. arXiv:1611.07004
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)
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). doi:10.1007/978-3-319-24574-4_28
Almazroa, A., Burman, R., Raahemifar, K., Lakshminarayanan, V.: Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J. Ophthalmol. 2015, 28 (2015). doi:10.1155/2015/180972. Article ID 180972
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Aquino, A., Gegundez-Arias, M., Marin, D.: Detecting the optic disc boundary in digital fundus images using morphological edge detection and feature extraction techniques. IEEE Trans. Med. Imaging 20(11), 1860–1869 (2010)
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., et al.: Optic nerve head segmentation. IEEE Trans. Med. Imaging 23(2), 256264 (2004)
Xu, J., Chutatape, O., Sung, E., Zheng, C., Chew, P.: Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognit. 40(7), 20632076 (2007)
Joshi, G.D., Sivaswamy, J., Krishnadas, S.R.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. Med. Imaging 30(6), 1192–1205 (2011)
Zheng, Y., Stambolian, D., O’Brien, J., Gee, J.C.: Optic disc and cup segmentation from color fundus photograph using graph cut with priors. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 75–82. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_10
Chen, X., Xu, Y., Yan, S., Wong, D.W.K., Wong, T.Y., Liu, J.: Automatic feature learning for glaucoma detection based on deep learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 669–677. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_80
Cheng, J., Liu, J., et al.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 32(6), 1019–1032 (2013)
Zilly, J., Buhmann, J.M., Mahapatra, D.: Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput. Med. Imaging Graph. 55, 2841 (2017)
Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_17
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shankaranarayana, S.M., Ram, K., Mitra, K., Sivaprakasam, M. (2017). Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-67561-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67560-2
Online ISBN: 978-3-319-67561-9
eBook Packages: Computer ScienceComputer Science (R0)