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Open AccessArticle

Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes

1
Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain
2
VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
3
Instituto Oftalmológico Victoria de Rojas, 15009 A Coruña, Spain
4
Hospital HM Rosaleda, 15701 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5269; https://doi.org/10.3390/s19235269
Received: 4 October 2019 / Revised: 26 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM’s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91.9%. Regarding the post-processing stage, mean specificity values of 91.9% and 99.0% were obtained from volumes with and without the ERM’s presence, respectively. View Full-Text
Keywords: computer-aided diagnosis; retinal imaging; optical coherence tomography; epiretinal membrane computer-aided diagnosis; retinal imaging; optical coherence tomography; epiretinal membrane
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MDPI and ACS Style

Baamonde, S.; de Moura, J.; Novo, J.; Charlón, P.; Ortega, M. Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes. Sensors 2019, 19, 5269.

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