Automatic Identification of Diabetic Macular Edema Biomarkers Using Optical Coherence Tomography Scans
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Automatic Identification of Diabetic Macular Edema Biomarkers Using Optical Coherence Tomography ScansAutor(es)
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2020Cita bibliográfica
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
Resumo
[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.
Palabras chave
Computer-aided diagnosis
Optical Coherence Tomography
Diabetic Macular Edema
Biomarkers
Optical Coherence Tomography
Diabetic Macular Edema
Biomarkers
Descrición
This version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-45096-0_31. 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019, Las Palmas de Gran Canaria, Spain, February 17–22, 2019.
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© 2020 Springer Nature Switzerland AG
ISSN
0302-9743
ISBN
978-3-030-45095-3 978-3-030-45096-0