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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-45096-0_31
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