Automatic Identification and Characterization of the Epiretinal Membrane in OCT Images
| Autor: | Sergio Baamonde, Pablo Charlón, Jorge Novo, Joaquim de Moura, Marcos Ortega |
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| Rok vydání: | 2019 |
| Předmět: |
Visual acuity
genetic structures Computer science Image processing Feature selection Vitreomacular traction Article Image analysis Optical coherence tomography Laser therapy medicine Medical imaging Modality (human–computer interaction) medicine.diagnostic_test business.industry Pattern recognition medicine.disease Atomic and Molecular Physics and Optics eye diseases Identification (information) Artificial intelligence sense organs Epiretinal membrane business Biotechnology |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname |
| Popis: | [Abstract] Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%. Ministerio de Economía, Industria y Competitividad, Gobierno de España (DPI2015-69948-R); Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia (ED431C 2016-047, ED431G/01); Instituto de Salud Carlos III (ISCIII) (DTS18/00136). Xunta de Galicia; ED431C 2016-047 Xunta de Galicia; ED431G/01 |
| Databáze: | OpenAIRE |
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