Deep Learning-Based Approach for the Semantic Segmentation of Bright Retinal Damage
- 1.2k Downloads
Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon U-Net architecture. This encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. The proposed method was validated on E-OPHTHA and DIARETDB1 public databases achieving promising results compared to current state-of-the-art methods.
KeywordsSemantic segmentation Deep learning Fundus images Exudates U-Net
This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT-2016-2017, 732613]. The work of Adrián Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
- 1.World Health Organization: Diabetes fact sheet. Sci. Total Environ. 20, 1–88 (2011)Google Scholar
- 2.Verma, L., Prakash, G., Tewari, H.K.: Diabetic retinopathy: time for action. No complacency please! Bull. World Health Organ. 80(5), 419–419 (2002)Google Scholar
- 5.Haloi, M., Dandapat, S., Sinha, R.: A Gaussian scale space approach for exudates detection, classification and severity prediction. arXiv preprint arXiv:1505.00737 (2015)
- 14.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_28CrossRefGoogle Scholar
- 15.Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation, pp. 1–23. arXiv preprint arXiv:1704.06857 (2017)
- 16.Deng, Z., Fan, H., Xie, F., Cui, Y., Liu, J.: Segmentation of dermoscopy images based on fully convolutional neural network. In: IEEE International Conference on Image Processing (ICIP 2017), pp. 1732–1736. IEEE (2017)Google Scholar
- 17.Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE (2014)Google Scholar
- 18.Li, W., Qian, X., Ji, J.: Noise-tolerant deep learning for histopathological image segmentation, vol. 510 (2017)Google Scholar