Segmentation of OCT and OCT-A Images using Convolutional Neural Networks

ABSTRACT Segmentation is vital in Optical Coherence Tomography Angiography (OCT-A) images. The separation and distinction of the different parts that build the macula simplify the subsequent detection of observable patterns/illnesses in the retina. In this work, we carried out multi-class image segmentation where the best characteristics are highlighted in the appropriate plexuses by comparing different neural network architectures, including U-Net, ResU-Net, and FCN. We focus on two critical zones: retinal vasculature (RV) and foveal avascular zone (FAZ). The precision obtained from the RV and FAZ segmentation over 316 OCT-A images from the OCT-A 500 database at 93.21% and 92.59%, where the FAZ was segmented with an accuracy of 99.83% for binary classification.

Bibliographic Details

Main Authors:	Cisneros-Guzmán,Fernanda, Toledano-Ayala,Manuel, Tovar-Arriaga,Saúl, Rivas-Araiza,Edgar A.
Format:	Digital revista
Language:	English
Published:	Sociedad Mexicana de Ingeniería Biomédica 2022
Online Access:	http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-95322022000300102
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