Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images

• Published in: Kompʹûternaâ optika Vol. 47; no. 5; pp. 824 - 831
• Main Authors: Demin, N.S., Ilyasova, N.Y., Paringer, R.A., Kirsh, D.V.
• Format: Journal Article
• Language: English
• Published: Samara National Research University 01.10.2023
• Subjects: artificial intelligence; diabetic retinopathy; fundus image; image processing; laser coagulation; neural network; segmentation
• ISSN: 0134-2452; 2412-6179
• DOI: 10.18287/2412-6179-CO-1283

The paper presents main aspects of the application of artificial intelligence in ophthalmology for the diagnosis and treatment of eye diseases, considering the problem of semantic segmentation of fundus images as an example. The classic approach to semantic segmentation on the basis of textural features is compared to the proposed approach based on neural networks. Basic problems of using the neural network approach in biomedicine are formulated. We propose a new method for selecting an optimal zone of laser exposure for laser coagulation based on two neural networks. The first network is used for detecting anatomical objects in the fundus and the second one is used for selecting the area of macular edema. The region of interest is formed from the edema area while taking into account the location of anatomical objects in it. A comparative analysis of sev-eral architectures of neural networks for solving the problem of selecting the edema area is carried out. The best results in the selection of the edema area are shown by the neural network architecture of Unet++.