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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

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

Li Liu¹, Simon S Gao², Steven T Bailey²

¹Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA ; College of Physics and Electronics, Shandong Normal University, Jinan, China.

Biomedical Optics Express

|September 30, 2015

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An automated algorithm accurately detects choroidal neovascularization (CNV) area using optical coherence tomography angiography, aiding age-related macular degeneration assessment. This method improves upon manual delineation by addressing image artifacts and CNV variability.

Area of Science:

Ophthalmology
Medical Imaging
Biomedical Engineering

Background:

Optical coherence tomography angiography (OCTA) visualizes choroidal neovascularization (CNV) in age-related macular degeneration (AMD).
Accurate identification and quantification of CNV area are crucial for clinical disease assessment and management.

Purpose of the Study:

To present an automated algorithm for detecting and quantifying CNV area from OCTA scans.
To overcome challenges in CNV detection, including projection artifacts and inherent heterogeneity.

Main Methods:

The algorithm utilizes denoising techniques to enhance image quality.
A saliency detection model is employed for robust CNV area identification.
Qualitative and quantitative evaluations were performed on OCTA scans from seven participants.

Keywords:

(100.0100) Image processing (170.3880) Medical and biological imaging (170.4500) Optical coherence tomography

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Doppler Optical Coherence Tomography of Retinal Circulation

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Published on: September 18, 2012

Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography

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Published on: February 18, 2022

Main Results:

The automated algorithm demonstrated strong agreement with manual delineation of CNV area.
The method effectively addressed projection artifacts and CNV heterogeneity in the analyzed scans.
Results indicate the algorithm's potential for reliable CNV quantification.

Conclusions:

The developed automated algorithm offers a promising tool for accurate CNV area detection in OCTA images.
This technology can aid clinicians in assessing disease severity and monitoring treatment response in AMD patients.
Further validation on larger datasets is warranted to confirm its clinical utility.