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


  • Fernanda Cisneros-Guzmán Universidad Autónoma de Querétaro, México
  • Manuel Toledano-Ayala Universidad Autónoma de Querétaro, México
  • Saúl Tovar-Arriaga Universidad Autónoma de Querétaro, México
  • Edgar A. Rivas-Araiza Universidad Autótoma de Querétaro, México



OCT-A segmentation, ResU-Net, FCN segmentation, Convolutional Neural Network


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.


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How to Cite

Cisneros-Guzmán, F., Toledano-Ayala, M., Tovar-Arriaga, S., & Rivas-Araiza, E. A. (2022). Segmentation of OCT and OCT-A Images using Convolutional Neural Networks. Revista Mexicana De Ingenieria Biomedica, 43(3), 15–24.



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