Resumo

CISNEROS-GUZMAN, Fernanda; TOLEDANO-AYALA, Manuel; TOVAR-ARRIAGA, Saúl  e  RIVAS-ARAIZA, Edgar A.. Segmentation of OCT and OCT-A Images using Convolutional Neural Networks. Rev. mex. ing. bioméd [online]. 2022, vol.43, n.3, 1280.  Epub 28-Abr-2023. ISSN 2395-9126.  https://doi.org/10.17488/rmib.43.3.2.

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.

Palavras-chave : OCT-A segmentation; ResU-Net; FCN segmentation; Convolutional Neural Network.

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