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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning.

Mohammad Tariqul Islam¹, Ferdaus Ahmed², Mowafa Househ³

¹Southern Connecticut State University, Connecticut, USA.

Studies in Health Technology and Informatics

|June 30, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning method for automatically detecting the optical disc in retinal images. The approach achieves over 99% accuracy, aiding in health assessments.

Keywords:

CNN Qatar Biobank Retina Segmentation

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

The optical disc is a critical area in retinal fundus images for assessing eye health.
Accurate identification of the optical disc is essential for diagnosing various ocular conditions.
Manual identification can be time-consuming and subjective.

Purpose of the Study:

To develop and evaluate a deep learning-based automated method for optical disc segmentation in retinal images.
To improve the accuracy and efficiency of optical disc detection compared to existing methods.

Main Methods:

Utilized multiple public-domain datasets of human retinal fundus images.
Formulated the task as an image segmentation problem.
Employed an attention-based residual U-Net architecture for precise localization.

Main Results:

Achieved over 99% pixel-level accuracy in optical disc detection.
Obtained a Matthew's Correlation Coefficient of approximately 95%.
Demonstrated superior performance compared to U-Net variants with different encoder CNN architectures across multiple metrics.

Conclusions:

The proposed attention-based residual U-Net is highly effective for automated optical disc segmentation.
This deep learning approach offers a robust and accurate tool for analyzing retinal images.
The method has significant potential for clinical applications in ophthalmology and telemedicine.