# Cup and Disc Segmentation in Smartphone Handheld Ophthalmoscope Images with a Composite Backbone and Double Decoder Architecture

## TL;DR

This paper introduces a deep learning model to accurately segment optic disc and cup structures in smartphone ophthalmoscope images, aiding in glaucoma diagnosis.

## Contribution

The novel approach combines a composite backbone with a double decoder architecture and a new loss weight combination method for improved segmentation.

## Key findings

- The model achieved 95.92% Dice score for optic disc segmentation and 85.30% for the optic cup.
- IoU scores reached 92.22% for the optic disc and 75.68% for the optic cup on the BrG dataset.

## Abstract

Glaucoma is a visual disease that affects millions of people, and early diagnosis can prevent total blindness. One way to diagnose the disease is through fundus image examination, which analyzes the optic disc and cup structures. However, screening programs in primary care are costly and unfeasible. Neural network models have been used to segment optic nerve structures, assisting physicians in this task and reducing fatigue. This work presents a methodology to enhance morphological biomarkers of the optic disc and cup in images obtained by a smartphone coupled to an ophthalmoscope through a deep neural network, which combines two backbones and a dual decoder approach to improve the segmentation of these structures, as well as a new way to combine the loss weights in the training process. The models obtained were numerically evaluated through Dice and IoU measures. The dice values obtained in the experiments reached a Dice of 95.92% and 85.30% for the optical disc and cup and an IoU of 92.22% and 75.68% for the optical disc and cup, respectively, in the BrG dataset. These findings indicate promising architectures in the fundus image segmentation task.