Abstract
Optical Coherence Tomography (OCT) imaging has revolutionized the daily clinical practice, especially in the field of ophthalmology. Diabetic Macular Edema (DME) is one of the most important complications of diabetes and a leading cause of preventable blindness in the developed countries. In this way, a precise identification and analysis of DME biomarkers allow the clinical specialists to make a more accurate diagnosis and treatment of this relevant ocular disease.
Thus, in this work, we present a computational system for the automatic identification and extraction of DME biomarkers by the analysis of OCT scans, following the clinical classification of reference in the ophthalmological field. The presented method was validated using a dataset composed by 40 OCT images that were retrieved from different patients. Satisfactory results were obtained, providing a consistent and coherent set of different computational biomarkers that can help the clinical specialists in their diagnostic procedures.
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
Similar content being viewed by others
References
Lee, R., Wong, T.Y., Sabanayagam, C.: Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis. 2(1), 17 (2015)
Romero-Aroca, P.: Managing diabetic macular edema: the leading cause of diabetes blindness. World J. Diabetes 2(6), 98 (2011)
de Moura, J., Novo, J., Charlón, P., Barreira, N., Ortega, M.: Enhanced visualization of the retinal vasculature using depth information in OCT. Med. Biol. Eng. Comput. 55(12), 2209–2225 (2017). https://doi.org/10.1007/s11517-017-1660-8
de Moura, J., Novo, J., Rouco, J., Penedo, M.G., Ortega, M.: Automatic identification of intraretinal cystoid regions in optical coherence tomography. In: ten Teije, A., Popow, C., Holmes, J.H., Sacchi, L. (eds.) AIME 2017. LNCS (LNAI), vol. 10259, pp. 305–315. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59758-4_35
de Moura, J., Novo, J., Penas, S., Ortega, M., Silva, J., Mendonça, A.M.: Automatic characterization of the serous retinal detachment associated with the subretinal fluid presence in optical coherence tomography images. Procedia Comput. Sci. 126, 244–253 (2018)
Schmitt, J.: Optical coherence tomography (OCT): a review. IEEE J. Sel. Top. Quantum Electron. 5(4), 1205–1215 (1999)
Otani, T., Kishi, S., Maruyama, Y.: Patterns of diabetic macular edema with optical coherence tomography. Am. J. Ophthalmol. 127(6), 688–693 (1999)
Panozzo, G., et al.: Diabetic macular edema: an OCT-based classification. In: Seminars in Ophthalmology, vol. 19, pp. 13–20. Taylor & Francis (2004)
Sidibé, D., et al.: An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Comput. Methods Programs Biomed. 139, 109–117 (2017)
Quellec, G., Lee, K., Dolejsi, M., Garvin, M., Abramoff, M., Sonka, M.: Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula. IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010)
Wilkins, J., et al.: Characterization of epiretinal membranes using optical coherence tomography. Ophthalmology 103(12), 2142–2151 (1996)
González-López, A., de Moura, J., Novo, J., Ortega, M., Penedo, M.G.: Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model. Heliyon 5(2), e01271 (2019)
Samagaio, G., Estévez, A., de Moura, J., Novo, J., Fernández, I., Ortega, M.: Automatic macular edema identification and characterization using OCT images. Comput. Methods Programs Biomed. 163, 47–63 (2018)
Othman, S., Manan, F., Zulkarnain, A., Mohamad, Z., Ariffin, A.: Macular thickness as determined by optical coherence tomography in relation to degree of myopia, axial length and vitreous chamber depth in malay subjects. Clin. Exp. Optom. 95(5), 484–491 (2012)
Baamonde, S., de Moura, J., Novo, J., Ortega, M.: Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10305, pp. 222–235. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59153-7_20
Acknowledgments
This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds through the DTS18/00136 research project and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
de Moura, J. et al. (2020). Automatic Identification of Diabetic Macular Edema Biomarkers Using Optical Coherence Tomography Scans. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-45096-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45095-3
Online ISBN: 978-3-030-45096-0
eBook Packages: Computer ScienceComputer Science (R0)