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Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images.

Anindita Septiarini¹, Hamdani Hamdani¹, Emy Setyaningsih²

¹Department of Informatics, Faculty of Engineering, Mulawarman University, Samarinda, Indonesia.

Healthcare Informatics Research

|May 16, 2023

Summary

This study introduces an automated method for segmenting optic discs in retinal images using deep learning. The convolutional neural network (CNN) approach achieved high accuracy, aiding glaucoma feature extraction.

Keywords:

Computer Vision Fundus Glaucoma Image Processing Optic Neuropathy

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

The optic disc is a critical structure in retinal fundus images.
Accurate optic disc segmentation is vital for glaucoma feature extraction.
Current methods may lack efficiency or precision.

Purpose of the Study:

To develop and evaluate an automated optic disc segmentation method.
To utilize deep learning, specifically convolutional neural networks (CNNs), for this task.
To improve the accuracy and efficiency of optic disc identification in retinal images.

Main Methods:

A CNN-based approach using MobileNetV2 and a single-shot multibox detector was employed.
Retinal fundus images from private (350 images) and public (REFUGE dataset) sources were used.
Image pre-processing included augmentation, resizing, and normalization, followed by U-Net model application for segmentation.

Main Results:

The method achieved high segmentation accuracy on both private and public datasets.
F1-scores, Dice scores, and Intersection over Union (IoU) values exceeded 0.97 on the private dataset.
Comparable high scores (F1: 0.9854, Dice: 0.9838, IoU: 0.9712) were obtained on the REFUGE dataset.

Conclusions:

The proposed automated method effectively segments the optic disc area.
The results closely matched manual segmentation by ophthalmologists.
This deep learning approach shows significant potential for clinical implementation in glaucoma diagnosis.