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17 September 2020 Deep learning algorithm for generating optical coherence tomography angiography (OCTA) maps of the retinal vasculature
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Abstract
We developed a deep learning system for inferring detailed retinal blood flow in structural optical coherence tomography (OCT) images. Motivations include enhanced diagnosis of retinal diseases and reducing time and cost of acquiring OCT angiography (OCTA) images. Using OCTA images as ground truth, we trained a conditional generative adversarial network (cGAN) to predict capillaries from OCT cross-sections. The inferred cross-sections and resulting en-face blood flow map images show comparable detail of small capillaries to the target images. The results demonstrate the potential of cGANs in inferring blood flow maps from new and existing retinal OCT datasets.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pei Lin Li, Céline O'Neil, Samin Saberi, Kenneth Sinder, Kathleen Wang, Bingyao Tan, Zohreh Hosseinaee, Kostadinka Bizhevat, and Vasudevan Lakshminarayanan "Deep learning algorithm for generating optical coherence tomography angiography (OCTA) maps of the retinal vasculature", Proc. SPIE 11511, Applications of Machine Learning 2020, 1151109 (17 September 2020); https://doi.org/10.1117/12.2568629
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