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Updated: May 4, 2026

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

Advanced OCTA imaging segmentation: Unsupervised, non-linear retinal vessel detection using modified self-organizing

Ahmed Alksas¹, Ahmed Sharafeldeen¹, Hossam Magdy Balaha¹

¹Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.

Computer Methods and Programs in Biomedicine

|July 13, 2024

Summary

This study introduces an automated method for segmenting retinal vascular systems in Optical Coherence Tomography Angiography (OCTA) images using a novel two-level joint Markov-Gibbs Random Field (MGRF) model, improving disease detection for Computer-Aided Diagnosis (CAD) systems.

Keywords:

Joint MGRF LCDG MAP-based optimization OCTA Self-organizing maps Unsupervised automatic segmentation

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Last Updated: May 4, 2026

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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Published on: March 26, 2020

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

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

Published on: November 30, 2022

Area of Science:

Medical Imaging
Computer Vision
Biomedical Engineering

Background:

Accurate segmentation of the retinal vascular system is crucial for diagnosing eye diseases.
Optical Coherence Tomography Angiography (OCTA) provides high-resolution images of retinal vasculature.
Developing automated methods for OCTA image analysis is essential for efficient Computer-Aided Diagnosis (CAD).

Purpose of the Study:

To propose a fully automated and unsupervised stochastic segmentation approach for retinal vascular system detection in OCTA images.
To leverage a two-level joint Markov-Gibbs Random Field (MGRF) model for enhanced segmentation accuracy.
To facilitate the development of CAD systems for retinal disease detection.

Main Methods:

A novel probabilistic model based on Linear Combination of Discrete Gaussian (LCDG) was used to model OCTA image appearance.
A modified Expectation Maximization (EM) algorithm estimated LCDG model parameters.
A two-level joint MGRF model, with analytically estimated parameters and modified self-organizing maps, performed unsupervised segmentation.

Main Results:

The proposed segmentation framework achieved a Dice similarity coefficient of 0.92 ± 0.03.
A 95-percentile bidirectional Hausdorff distance of 0.69 ± 0.25 was obtained.
An accuracy of 0.93 ± 0.03 confirmed the superior performance of the approach.

Conclusions:

The developed unsupervised and fully automated segmentation approach demonstrates superior performance in detecting the vascular system from OCTA images.
This method offers a significant advancement over traditional techniques, providing more accurate segmentation results.
The approach holds substantial potential for improving CAD systems in the detection of retinal diseases.