Abstract
Optic disc and cup segmentation on ocular fundus images is an important prerequisite for diagnosing glaucoma. For the segmentation of optic disc (OD) and optic cup (OC), many previously proposed deep learning methods typically utilize monoscopic view images that lack spatial depth information, limiting their diagnostic ability and overall performance. According to ophthalmologists’ clinical insights, stereoscopic view of ocular fundus contains great potential to improve optic cup segmentation. We propose a depth mapping hybrid (DeMaH) deep learning method that effectively adopts depth mappings to segment OD and OC (ODC) on ocular fundus images. Experimental results demonstrate that our method achieves significant improvement on ODC segmentation, especially OC segmentation, validating the effectiveness of our method to incorporate clinical prior knowledge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aquino, A., Gegúndez-Arias, M.E., Marín, D.: Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans. Med. Imaging 29, 1860–1869 (2010)
Berger, K., Voorhies, R., Matthies, L.H.: Depth from stereo polarization in specular scenes for urban robotics. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1966–1973. IEEE, Singapore (2017)
Chan, H.P., et al.: Assessment of breast lesions on stereoscopic and monoscopic digital specimen mammograms: an roc study. In: SPIE Medical Imaging, p. 428. SPIR, San Diego (2004)
hen, Z., Sun, X., Wang, L., Yu, Y., Huang, C.: A deep visual correspondence embedding model for stereo matching costs. In: IEEE International Conference on Computer Vision, pp. 972–980. IEEE, Santiago (2015)
Cheng, J., et al.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 32, 1019–1032 (2013)
Ding, F., et al.: Hierarchical attention networks for medical image segmentation. CoRR abs/1911.08777
Ding, F., et al.: High-order attention networks for medical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 253–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_25
Feng, S., Zhuo, Z., Pan, D., Tian, Q.: Ccnet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features. Neurocomputing 392, 268–276 (2020)
Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37, 1597–1605 (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, M.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
Fumero, F., Alayón, S., Sanchez, J.L., Sigut, J., Gonzalez-Hernandez, M.: RIM-ONE: an open retinal image database for optic nerve evaluation. In: 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. IEEE, Bristol (2011)
Goatman, K.A., Fleming, A.D., Philip, S., Williams, G.J., Olson, J.A., Sharp, P.F.: Detection of new vessels on the optic disc using retinal photographs. IEEE Trans. Med. Imaging 30(4), 972–979 (2011)
Graber, G., Balzer, J., Soatto, S., Pock, T.: Efficient minimal-surface regularization of perspective depth maps in variational stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–520. IEEE, Boston (2015)
Gu, Z., et al.: CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Jonas, J.B., Bergua, A., Schmitz-Valckenberg, P., Papastathopoulos, K.I., Budde, W.M.: Ranking of optic disc variables for detection of glaucomatous optic nerve damage. Invest. Ophthalmol. Vis. Sci. 41, 1764–1773 (2000)
Joshi, G.D., Sivaswamy, J., Krishnadas, S.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. Med. Imaging 30(6), 1192–1205 (2011)
Khamis, S., Fanello, S., Rhemann, C., Kowdle, A., Valentin, J., Izadi, S.: StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 596–613. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_35
Morgan, J.E.: Digital imaging of the optic nerve head: mono-scopic and stereoscopic analysis. Br. J. Ophthalmol. 89(7), 879–884 (2005)
Noor, N., Khalid, N., Ariff, N.: Optic cup and disc color channel multi-thresholding segmentation, pp. 530–53. IEEE, Penang (2013)
Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Iterative vessel segmentation of fundus images. IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015)
Saxena, A., Schulte, J., Ng, A.Y., et al.: Depth estimation using monocular and stereo cues. In: Proceedings of the 20th international joint conference on Artificial intelligence (IJCAI 2007), pp. 2197–2220. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal. 27, 618–624 (2017)
Shankaranarayana, S.M., Ram, K., Mitra, K., Sivaprakasam, M.: Joint optic disc and cup segmentation using fully convolutional and adversarial networks. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA -2017. LNCS, vol. 10554, pp. 168–176. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67561-9_19
Sharma, N., Verma, A.: Segmentation and detection of optic disc using k-means clustering. Int. J. Sci. Eng. Res. 6(8), 237–240 (2015)
Shi, B., Matsushita, Y., Wei, Y., Xu, C., Tan, P.: Self-calibrating photometric stereo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1118–112. IEEE, San Francisco (2010)
Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted Res-UNet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327–331. IEEE, Hangzhou (2018)
Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., Quan, L.: Recurrent MVSNet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5534 (2019)
Zhu, D., Smith, W.A.: Depth from a polarisation + RGB stereo pair. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7586–7595. IEEE, Long Beach (2019)
Acknowledgement
This work was supported by the Beijing Natural Science Foundation (No. 4192029), and the National Natural Science Foundation of China (61773385, 61672523).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, G. et al. (2021). Depth Mapping Hybrid Deep Learning Method for Optic Disc and Cup Segmentation on Stereoscopic Ocular Fundus. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-86365-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86364-7
Online ISBN: 978-3-030-86365-4
eBook Packages: Computer ScienceComputer Science (R0)