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Retinal Artery and Vein Classification for Automatic Vessel Caliber Grading.

Alauddin Bhuiyan, Md Akter Hussain, Tien Y Wong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    This study introduces an automated method for classifying retinal arteries and veins using deep learning. The novel framework achieves 95% accuracy, enabling efficient and repeatable caliber measurements for retinal health assessment.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Accurate identification and caliber measurement of retinal arteries and veins are crucial for diagnosing various ocular and systemic diseases.
    • Current manual methods are time-consuming, subjective, and lack repeatability, hindering large-scale image analysis.

    Purpose of the Study:

    • To develop and validate a novel, automated framework for classifying retinal blood vessels as arteries or veins.
    • To enable precise and efficient measurement of vessel caliber for calculating Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE).

    Main Methods:

    • Utilized a deep learning-based segmentation algorithm to extract the retinal vascular network from fundus images.
    • Employed vessel crossover information and color/intensity profiles for artery-vein classification.
    • Mapped the vascular network to identify individual vessels for caliber measurement.

    Main Results:

    • Achieved a 95% accuracy in classifying retinal arteries and veins compared to a human grader.
    • Demonstrated a high correlation (0.85 for CRAE, 0.92 for CRVE) with an established semi-automated caliber grading system.
    • The automated method provides efficient and repeatable vessel caliber measurements.

    Conclusions:

    • The proposed automated framework offers a highly accurate and reliable method for retinal artery and vein classification.
    • This technology can significantly improve the efficiency and objectivity of quantitative vascular analysis in ophthalmology.
    • Automated caliber grading has the potential to enhance early detection and monitoring of diseases affecting retinal vasculature.