Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography

Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography

Reliable outcome measures are required for clinical trials investigating novel agents for preventing progression of capillary non-perfusion (CNP) in retinal vascular diseases. Currently, accurate quantification of topographical distribution of CNP on ultrawide field fluorescein angiography (UWF-FA)...

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Bibliographic Details
Main Authors: Joan M. Nunez do Rio, Piyali Sen, Rajna Rasheed, Akanksha Bagchi, Luke Nicholson, Adam M. Dubis, Christos Bergeles, Sobha Sivaprasad
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Journal of Clinical Medicine
Subjects:
retinal non-perfusion
fluorescein angiography
image segmentation
Online Access:https://www.mdpi.com/2077-0383/9/8/2537

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https://www.mdpi.com/2077-0383/9/8/2537

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