Abstract
Peripapillary atrophy (PPA) is a clinical finding that reflects atrophy of the retinal layer and retinal pigment epithelium. It is very important to segment PPA area as it indicates the progress of eye diseases such as myopia and glaucoma, while it is a challenging task to segment PPA due to the irregular and ambiguous boundaries. In this paper, a boundary guidance deep learning method is introduced to segment PPA area to obtain precise shape. We propose a boundary guidance block together with a contour loss function to improve the PPA segmentation performance on boundaries. Our approach is evaluated on a clinical dataset. The F1-score, IOU and Hausdorff distance of our method performance is 80.06%, 67.29%, 5.4934 respectively. Compared with other methods, our method achieves the best performance both qualitatively and quantitatively. Our proposed method can work well on retinal images with narrow PPA even with small training set.
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References
Manjunath, V., Shah, H., Fujimoto, J.G., Duker, J.S.: Analysis of peripapillary atrophy using spectral domain optical coherence tomography. Ophthalmology 118(3), 531–536 (2011)
Jonas, J.B., Gusek, G.C., Naumann, G.O.H.: Optic disk morphometry in high myopia. Graefe’s Arch. Clin. Exp. Ophthalmol. 226(6), 587–590 (1988)
Lu, C.K., Tang, T.B., Alan, F.M., Lauda, A., Dhillon, B.: Automatic parapapillary atrophy shape detection and quantification in colour fundus images. In: 2010 Biomedical Circuits and Systems Conference (BioCAS), pp. 86–89. Paphos (2010)
Patil, D.D., Deore, S.G.: Medical image segmentation: a review. Int. J. Comput. Sci. Mob. Comput. 2(1), 22–27 (2013)
Narkhede, H.P.: Review of image segmentation techniques. Int. J. Sci. Modern Eng. 1(8), 54–61 (2013)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
Badrinarayanan, V., Kendal, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Joshi, G.D., Sivaswamy, J., Krishnadas, S.R.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. Med. Imag. 30(6), 1192–1205 (2011)
Yu, H., et al.: Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans. Inform. Technol. Biomed. 16(4), 644–657 (2012)
Bharkad, S.: Automatic segmentation of optic disk in retinal images. Biomed. Signal Process. Control 31, 483–498 (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.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, pp. 140–148. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_17
Wang, L., Liu, H., Lu, Y., Chen, H., Zhang, J., Pu, J.: A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed. Signal Process. Control 51, 82–89 (2019)
Li, H., Li, H., Kang, J., Feng, Y., Xu, J.: Automatic detection of parapapillary atrophy and its association with children myopia. Comput. Methods Programs Biomed. 183, 105090 (2020)
Chai, Y., Liu, H., Xu, J.: A new convolutional neural network model for peripapillary atrophy area segmentation from retinal fundus images. Appl. Soft Comput. J. 86, 1–11 (2020)
Li, H., et al.: Automatic location of optic disk in retinal images. In: Proceedings 2001 International Conference on Image Processing, vol. 2, pp. 837–840 (2001)
Acknowledgment
The research work is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 82072007) and China Postdoctoral Science Foundation (No. 2020M680387).
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Li, M., Zhao, H., Xu, J., Li, H. (2021). Peripapillary Atrophy Segmentation with Boundary Guidance. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_11
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DOI: https://doi.org/10.1007/978-3-030-87000-3_11
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