Abstract
Exudates in retinal images are one of the early signs of the vision-threatening diabetic retinopathy and diabetic macular edema. Early diagnosis is very helpful in preventing the progression of the disease. In this work, we propose a fully automatic exudate segmentation method based on the state-of-the-art residual learning framework. With our proposed end-to-end architecture the training is done on small patches, but at the test time, the full sized segmentation is obtained at once. The small number of exudates in the training set and the presence of other bright regions are the limiting factors, which are tackled by our proposed importance sampling approach. This technique selects the misleading normal patches with a higher priority, and at the same time avoids the network to overfit to those samples. Thus, no additional post-processing is needed. The method was evaluated on three public datasets for both detecting and segmenting the exudates and outperformed the state-of-the-art techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alghamdi, H.S., Tang, H.L., Waheeb, S.A., Peto, T.: Automatic optic disc abnormality detection in fundus images: a deep learning approach. In: Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, Held in Conjunction with MICCAI, pp. 17–24 (2016)
Canévet, O., Jose, C., Fleuret, F.: Importance sampling tree for large-scale empirical expectation. In: International Conference on Machine Learning, pp. 1454–1462 (2016)
Harangi, B., Lazar, I., Hajdu, A.: Automatic exudate detection using active contour model and regionwise classification. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5951–5954 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
The International Council of Ophthalmology (ICO): ICO Guidelines for Diabetic Eye Care, January 2017
Kälviäinen, R., Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis, p. 61 (2007)
Nesterov, Y.: A method for unconstrained convex minimization problem with the rate of convergence O(1/k2). Sov. Math. Dokl. 27(2), 372–376 (1983)
Perdomo, O., Otalora, S., Rodríguez, F., Arevalo, J., González, F.A.: A novel machine learning model based on exudate localization to detect diabetic macular edema. In: Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, Held in Conjunction with MICCAI, pp. 137–144 (2016)
Pires, R., Jelinek, H.F., Wainer, J., Valle, E., Rocha, A.: Advancing bag-of-visual-words representations for lesion classification in retinal images. PloS One 9(6), e96814 (2014)
Prentašić, P., Lončarić, S.: Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput. Methods Programs Biomed. 137, 281–292 (2016)
Prentašić, P., Lončarić, S., Vatavuk, Z., Bencic, G., Subasic, M., Petkovic, T., Dujmovic, L., Malenica-Ravlic, M., Budimlija, N., Tadic, R.: Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. In: 8th International Symposium on Image and Signal Processing and Analysis, pp. 711–716. IEEE (2013)
Raman, R., Nittala, M.G., Gella, L., Pal, S.S., Sharma, T.: Retinal sensitivity over hard exudates in diabetic retinopathy. J. Ophthalmic Vis. Res. 10(2), 160 (2015)
Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., Laÿ, B., Danno, R., Cazuguel, G., Quellec, G., Lamard, M., Massin, P., et al.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. 18(7), 1026–1043 (2014)
Acknowledgement
This project has received funding from the European Union’s Seventh Framework Programme, Marie Curie Actions-Initial Training Network, under Grant Agreement No. 607643, “Metric Analysis For Emergent Technologies (MAnET)". It was also supported by the Hé Programme of Innovation, which is partly financed by the Netherlands Organization for Scientific Research (NWO) under Grant No. 629.001.003.
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
Abbasi-Sureshjani, S., Dashtbozorg, B., ter Haar Romeny, B.M., Fleuret, F. (2017). Boosted Exudate Segmentation in Retinal Images Using Residual Nets. 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_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-67561-9_24
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)