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Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images
N.S. Demin 1,2, N.Y. Ilyasova 1,2, R.A. Paringer 1,2, D.V. Kirsh 1,2

1 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151;
2 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 966 kB

DOI: 10.18287/2412-6179-CO-1283

Pages: 824-831.

Full text of article: Russian language.

Abstract:
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++.

Keywords:
fundus image, laser coagulation, diabetic retinopathy, image processing, segmentation, neural network, artificial intelligence.

Citation:
Demin NS, Ilyasova NY, Paringer RA, Kirsh DV. Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images. Computer Optics 2023; 47(5): 824-831. DOI: 10.18287/2412-6179-CO-1283.

Acknowledgements:
This work was funded by the Russian Foundation for Basic Research under RFBR grant # 19-29-01135 and the Ministry of Science and Higher Education of the Russian Federation within a government project of FSRC “Crystallography and Photonics” RAS.

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