Method of segmentation of OCT images using a convulsive neural network

The article analyzes the methods of segmentation of optical coherence tomography images, creates a convolutional neural network model U-Net, processes a series of test images from an open database, and compares the results of processing with other algorithms using the structural similarity index (SSIM). Pre-processing of test images to improve the quality of segmentation is also performed. Preprocessing of test images was also carried out to improve the quality of segmentation. In this work, the U-Net convolutional neural network was created, trained and applied. Existing methods of segmentation of optical coherence tomography images for the diagnosis and monitoring of ophthalmic diseases were considered. The advantages of using the U-Net deep convolutional neural network in comparison with classical methods, such as the Sobel operator and the Pruitt operator, were analyzed. Unlike classical algorithms, which have limited ability to adapt to noise, image heterogeneity and pathologies, U-Net provided higher accuracy of image segmentation.