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• W4286539651 abstract "Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed from the anatomy component, a deep learning model that takes the anatomy component as input can learn to segment vessels with a limited portion of the training images being manually labeled. Our method demonstrates state-of-the-art performance for OCTA vessel segmentation." @default.
• W4286539651 created "2022-07-22" @default.
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• W4286539651 date "2022-12-01" @default.
• W4286539651 modified "2023-10-14" @default.
• W4286539651 title "Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data" @default.
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• W4286539651 doi "https://doi.org/10.1109/tmi.2022.3193029" @default.
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