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Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage

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Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage

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dc.contributor.author Silva, Cristiana es_ES
dc.contributor.author Colomer, Adrián es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.date.accessioned 2019-07-24T09:39:16Z
dc.date.available 2019-07-24T09:39:16Z
dc.date.issued 2018-11-09
dc.identifier.isbn 978-3-030-03492-4
dc.identifier.uri http://hdl.handle.net/10251/124075
dc.description.abstract 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 UNet 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-theart methods. es_ES
dc.description.sponsorship This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an 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. es_ES
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation MINECO/BES-2014-067889 es_ES
dc.relation.ispartof Intelligent Data Engineering and Automated Learning – IDEAL 2018 es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;11314
dc.rights Reserva de todos los derechos es_ES
dc.subject Semantic segmentation es_ES
dc.subject Deep learning es_ES
dc.subject Fundus images es_ES
dc.subject Exudates es_ES
dc.subject U-Net es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-030-03493-1_18
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/732613/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Silva, C.; Colomer, A.; Naranjo Ornedo, V. (2018). Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 164-173. https://doi.org/10.1007/978-3-030-03493-1_18 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) es_ES
dc.relation.conferencedate Noviembre 21-23,2018 es_ES
dc.relation.conferenceplace Madrid, Spain es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-030-03493-1_18 es_ES
dc.description.upvformatpinicio 164 es_ES
dc.description.upvformatpfin 173 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\372960 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.relation.references World Health Organization: Diabetes fact sheet. Sci. Total Environ. 20, 1–88 (2011) es_ES
dc.relation.references Verma, L., Prakash, G., Tewari, H.K.: Diabetic retinopathy: time for action. No complacency please! Bull. World Health Organ. 80(5), 419–419 (2002) es_ES
dc.relation.references Sopharak, A.: Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J. Mod. Opt. 57(2), 124–135 (2010) es_ES
dc.relation.references Imani, E., Pourreza, H.R.: A novel method for retinal exudate segmentation using signal separation algorithm. Comput. Methods Programs Biomed. 133, 195–205 (2016) es_ES
dc.relation.references Haloi, M., Dandapat, S., Sinha, R.: A Gaussian scale space approach for exudates detection, classification and severity prediction. arXiv preprint arXiv:1505.00737 (2015) es_ES
dc.relation.references Welfer, D., Scharcanski, J., Marinho, D.R.: A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput. Med. Imaging Graph. 34(3), 228–235 (2010) es_ES
dc.relation.references Harangi, B., Hajdu, A.: Automatic exudate detection by fusing multiple active contours and regionwise classification. Comput. Biol. Med. 54, 156–171 (2014) es_ES
dc.relation.references Sopharak, A., Uyyanonvara, B., Barman, S.: Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Sensors 9(3), 2148–2161 (2009) es_ES
dc.relation.references Havaei, M., Davy, A., Warde-Farley, D.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017) es_ES
dc.relation.references Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imag. 35(11), 2369–2380 (2016) es_ES
dc.relation.references Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016) es_ES
dc.relation.references Gulshan, V., Peng, L., Coram, M.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016) es_ES
dc.relation.references 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) es_ES
dc.relation.references 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_28 es_ES
dc.relation.references 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) es_ES
dc.relation.references 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) es_ES
dc.relation.references 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) es_ES
dc.relation.references Li, W., Qian, X., Ji, J.: Noise-tolerant deep learning for histopathological image segmentation, vol. 510 (2017) es_ES
dc.relation.references Chen, H., Qi, X., Yu, L.: DCAN: deep contour-aware networks for object instance segmentation from histology images. Med. Image Anal. 36, 135–146 (2017) es_ES
dc.relation.references Walter, T., Klein, J.C., Massin, P., Erginay, A.: A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging 21(10), 1236–1243 (2002) es_ES
dc.relation.references Morales, S., Naranjo, V., Angulo, U., Alcaniz, M.: Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans. Med. Imaging 32(4), 786–796 (2013) es_ES
dc.relation.references Zhang, X., Thibault, G., Decencière, E.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. 18(7), 1026–1043 (2014) es_ES


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