Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks

Ivan Coronado, Samiksha Pachade, Emanuele Trucco, Rania Abdelkhaleq, Juntao Yan, Sergio Salazar-Marioni, Amanda Jagolino-Cole, Mozhdeh Bahrainian, Roomasa Channa, Sunil A. Sheth, Luca Giancardo

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Abstract

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

Original languageEnglish
Article number15325
Number of pages12
JournalScientific Reports
Volume13
DOIs
Publication statusPublished - 15 Sept 2023

Keywords

  • Computer science
  • Medical imaging
  • Retina

ASJC Scopus subject areas

  • General

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