Quantitative Characterization of Retinal Features in Translated OCTA

arXiv:2404.16133v1 Announce Type: cross
Abstract: Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware. Methods: The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease …

abstract adversarial arxiv cs.cv cs.lg features framework generative generative adversarial network hardware images machine machine learning network optical quantitative study translate translated type