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Related Experiment Video

Updated: Feb 24, 2026

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
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Automatic blood vessels segmentation based on different retinal maps from OCTA scans.

Nabila Eladawi1, Mohammed Elmogy1, Omar Helmy2

  • 1Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA.

Computers in Biology and Medicine
|August 15, 2017
PubMed
Summary

This study introduces an automated system for segmenting retinal blood vessels in Optical Coherence Tomography Angiography (OCTA) images. The method accurately identifies vascular networks in normal and diabetic retinas, aiding in diagnosing systemic vascular diseases.

Keywords:
Diabetic retinopathy (DR)Generalized Gauss-Markov random field (GGMRF)Higher-order spatial Markov-Gibbs random field (MGRF)Optical coherence tomography angiography (OCTA)Retinal blood vessels segmentation

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • The retinal vascular network is a key indicator of systemic vascular health.
  • Segmentation of retinal blood vessels aids in diagnosing vascular diseases.
  • Optical Coherence Tomography Angiography (OCTA) provides high-resolution retinal imaging.

Purpose of the Study:

  • To develop an automatic system for segmenting retinal blood vessels from OCTA images.
  • To segment vessels from both superficial and deep retinal maps in normal and diabetic cases.
  • To evaluate the system's accuracy and robustness for clinical applications.

Main Methods:

  • Noise reduction and contrast enhancement using the Generalized Gauss-Markov random field (GGMRF) model.
  • Joint Markov-Gibbs random field (MGRF) model integrating appearance, spatial, and prior probability models for segmentation.
  • Higher-order MGRF (HO-MGRF) and 1st-order intensity models to address low contrast.
  • Segmentation refinement using a 2D connectivity filter.

Main Results:

  • The system was trained and tested on 47 datasets (23 normal, 24 diabetic).
  • Achieved high accuracy with Dice Similarity Coefficient (DSC) of 95.04±3.75%.
  • Demonstrated robustness with Absolute Vessels Volume Difference (VVD) of 8.51±1.49% and Area Under the Curve (AUC) of 95.20±1.52%.

Conclusions:

  • The proposed automatic segmentation system shows significant promise for analyzing retinal vasculature in OCTA images.
  • The method effectively segments blood vessels in both normal and diabetic retinas.
  • Accurate segmentation can support early diagnosis and monitoring of systemic vascular conditions.