Purpose: to develop and evaluate the effectiveness of a neural network model for automatic segmentation of epiretinal membrane (ERM) in optical coherence tomography (OCT) images.

Materials and methods. The study includes 322 labeled macular OCT scans with signs of ERM: 167 from the private dataset of the “Professorskaya Plus” clinic and 155 from the public OCTDL dataset. Five architectures were selected for comparison: U-Net, Attention U-Net, TransUNet, LOCTSeg, and Tiny-UNet. Initial annotations were generated using a baseline U-Net model and underwent expert clinical validation. Segmentation performance was assessed using Dice coefficient and Intersection over Union (IoU). Annotation quality was ensured by three experienced ophthalmologists with over 10 years of clinical practice.

Results. All models demonstrated comparable Dice and IoU scores, with no statistically significant differences. Tiny-UNet showed the best balance of accuracy and computational efficiency: 570K parameters, 5× faster training per epoch than U-Net, and total training time of only 20 min. Segmentation accuracy reached Dice = 86.1 %, IoU = 78.6 %.

Conclusion. Tiny-UNet appears to be the optimal architecture for ERM segmentation tasks, offering high accuracy with minimal computational requirements. Its efficiency makes it suitable for clinical deployment, including in mobile and cloud-based telemedicine platforms.