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A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms
Min Gao1, Yukun Guo1, Tristan T Hormel1
1Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Ophthalmology Science
|October 24, 2022
Summary
A new deep learning method, CAVnet, accurately differentiates arteries and veins in retinal OCT angiography images. This tool aids in analyzing vascular changes in diseases like diabetic retinopathy and vein occlusion.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Accurate differentiation of retinal arteries and veins is crucial for diagnosing and monitoring various ocular diseases.
- Current methods for vessel segmentation in Optical Coherence Tomography Angiography (OCTA) can be labor-intensive and subjective.
Purpose of the Study:
- To develop and validate a deep learning-based method, CAVnet, for automated classification of arteries and veins in widefield OCTA images.
- To assess the performance of CAVnet in segmenting retinal vasculature down to precapillary arterioles and postcapillary venules.
Main Methods:
- A convolutional neural network (CAVnet) was trained using 240 OCTA angiograms from 88 eyes and tested on 302 angiograms from 144 eyes.
- The method processes montaged widefield OCTA en face images to output segmented arteries and veins, including their intersections.
- Performance was evaluated using sensitivity, specificity, F1-score, and Intersection over Union (IoU) against manually graded ground truth.
Main Results:
- CAVnet achieved high accuracy in classifying arteries (95.3% sensitivity, 99.6% specificity, 94.2% F1-score, 89.3% IoU) and veins (94.4% sensitivity, 99.7% specificity, 94.1% F1-score, 89.2% IoU).
- The algorithm demonstrated robustness across different OCT instruments and scan sizes, with good performance in differentiating vessels in diabetic retinopathy (DR) and branch retinal vein occlusion (BRVO) cases.
- Generated pixel-wise caliber and tortuosity maps revealed differences between healthy and diseased eyes, indicating clinical potential.
Conclusions:
- CAVnet effectively classifies retinal arteries and veins and their branches with high accuracy.
- The tool shows potential for analyzing vessel type-specific features in diseases like branch retinal artery occlusion and BRVO.
- Automated vessel classification and analysis using CAVnet could enhance clinical utility in ophthalmology.
Keywords:
BRVO, branch retinal vein occlusionCAVnet, classification of artery and vein networkCE, cross-entropyClassification of arteries and veinsDR, diabetic retinopathyDeep learningIoU, Intersection over UnionMeasurement of their caliber and tortuosityNPDR, nonproliferative diabetic retinopathyOCTA, OCT angiographyPDR, proliferative diabetic retinopathyRelated Concept Videos
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