Automatic Selection of the Optimal Zone for Laser Exposure According to the Fundus Images for Laser Coagulation

Nikita S. Demin (Login required)
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia
Samara National Research University, Russia

Nataly Yu. Ilyasova
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia
Samara National Research University, Russia

Rustam A. Paringer
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia
Samara National Research University, Russia


Paper #8988 received 13 Jun 2023; revised manuscript received 23 Oct 2023; accepted for publication 27 Oct 2023; published online 8 Dec 2023.

DOI: 10.18287/JBPE23.09.040308

Abstract

We analyzed a problem of extracting regions of interest (RoI) in eye-fundus images in the laser treatment for diabetic retinopathy (DR) using machine learning of deep neural networks aimed at higher-accuracy recognition of pathological and anatomical structures in the macula edema region. In this paper, we propose a method for automatic selection of the optimal zone of laser exposure based on images of the fundus for laser coagulation. Two neural networks were used to solve the problem. The first singled out anatomical objects in the fundus, and the second edema zone. The result was formed from the edema area, taking into account the location of anatomical objects on it.

Keywords

full convolutional neural networks; fundus imaging; macular edema; laser coagulation

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References


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