TY - JOUR AU - Cisneros-Guzmán, Fernanda AU - Toledano-Ayala, Manuel AU - Tovar-Arriaga, Saúl AU - Rivas-Araiza, Edgar A. PY - 2022/11/11 Y2 - 2024/03/28 TI - Segmentation of OCT and OCT-A Images using Convolutional Neural Networks JF - Revista Mexicana de Ingenieria Biomedica JA - Rev Mex Ing Biom VL - 43 IS - 3 SE - Research Articles DO - 10.17488/RMIB.43.3.2 UR - http://rmib.mx/index.php/rmib/article/view/1280 SP - 15-24 AB - <p>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.</p> ER -