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Retinal vascular analysis in a fully automated method for the segmentation of DRT edemas using OCT images.

Authors :
Moura, Joaquim de
Novo, Jorge
Charlón, Pablo
Fernández, María Isabel
Ortega, Marcos
Source :
Procedia Computer Science; 2019, Vol. 159, p600-609, 10p
Publication Year :
2019

Abstract

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows a complete analysis and evaluation of the main retinal structures and their histopathology properties. Diabetic Macular Edema (DME) implies the accumulation of intraretinal fluid within the macular region. Diffuse Retinal Thickening (DRT) edemas are considered a relevant case of DME disease, where the pathological regions are characterized by a "sponge-like" appearance and a reduced intraretinal reflectivity, being visible in OCT images. Additionally, the presence of other structures may alter the OCT image characteristics, confusing the pathological identification process. This is the case of the retinal vessels over all the eye fundus, whose presence produce shadow projections over the retinal layers that may hide the "sponge-like" appearance of the DRT edemas. Thus, in this paper, we present a proposal for the automatic extraction of DRT edemas, also using as reference the information provided by the automatic identifications of the retinal vessels in the OCT images. To do that, firstly, the system delimits three retinal regions of interest. These retinal regions facilitate the posterior identification of the vessel structures and the segmentation of the DRT regions. For the identification of the vessels structures, the method combined the localization of the upper bright vascular profiles with the presence of their corresponding lower dark vascular shadows. Finally, a learning strategy is implemented for the segmentation of the DRT edemas. Satisfactory results were obtained, reaching values of 0.8346 and 0.9051 of Jaccard index and Dice coefficient, respectively, for the extraction of the existing DRT edemas. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
159
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
139120339
Full Text :
https://doi.org/10.1016/j.procs.2019.09.215