1. SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration

    SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration

    Purpose: To compare area measurements between swept source optical coherence tomography angiography ( SSOCTA ), fluorescein angiography ( FA ), and indocyanine green angiography ( ICGA ) after applying a novel deep-learning-assisted algorithm for accurate image registration. Methods: We applied an algorithm for the segmentation of blood vessels in FA , ICGA , and SSOCTA images of 24 eyes with treatment-naive neovascular age-related macular degeneration. We trained a model based on U-Net and Mask R-CNN for each imaging modality using vessel annotations and junctions to estimate scaling, translation, and rotation. For fine-tuning of the registration, vessels and the elastix framework were used. Area, perimeter, and circularity measurements were ...

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