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DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis by Daho, Mostafa El Habib; Li, Yihao; Zeghlache, Rachid; Boité, Hugo Le; Deman, Pierre; Borderie, Laurent; Ren, Hugang; Mannivanan, Niranchana; Lepicard, Capucine; Cochener, Béatrice; Couturier, Aude; Tadayoni, Ramin; Conze, Pierre-Henri; Lamard, Mathieu; Quellec, Gwenolé is a scholarly article available to read on EtoBox.

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Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering enhanced structural and functional information (blood flow) with a wider field of view. This paper investigates automatic DR severity assessment using 3-D OCTA. A straightforward solution to this task is a 3-D neural network classifier. However, 3-D architectures have numerous parameters and typically require many training samples. A lighter solution consists in using 2-D neural network classifiers processing 2-D en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an approach mimics the way ophthalmologists analyze OCTA acquisitions: 1) en-face flow maps are often used to detect avascular zones and neovascularization, and 2) cross-sectional slices are commonly analyzed to detect macular edemas, for instance. However, arbitrary data reduction or selection might

Author

Daho, Mostafa El Habib; Li, Yihao; Zeghlache, Rachid; Boité, Hugo Le; Deman, Pierre; Borderie, Laurent; Ren, Hugang; Mannivanan, Niranchana; Lepicard, Capucine; Cochener, Béatrice; Couturier, Aude; Tadayoni, Ramin; Conze, Pierre-Henri; Lamard, Mathieu; Quellec, Gwenolé

Published

2024

Language

EN