@article{Cisneros-Guzmán_Toledano-Ayala_Tovar-Arriaga_Rivas-Araiza_2022, title={Segmentation of OCT and OCT-A Images using Convolutional Neural Networks}, volume={43}, url={https://rmib.mx/index.php/rmib/article/view/1280}, DOI={10.17488/RMIB.43.3.2}, abstractNote={<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>}, number={3}, journal={Revista Mexicana de Ingenieria Biomedica}, author={Cisneros-Guzmán, Fernanda and Toledano-Ayala, Manuel and Tovar-Arriaga, Saúl and Rivas-Araiza, Edgar A.}, year={2022}, month={Nov.}, pages={15–24} }