X-GAN: A Generative AI-Powered Unsupervised Model for High-Precision Segmentation of Retinal Main Vessels toward Early Detection of Glaucoma
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
Structural changes in main retinal blood vessels serve as critical biomarkers
for the onset and progression of glaucoma. Identifying these vessels is vital
for vascular modeling yet highly challenging. This paper proposes X-GAN, a
generative AI-powered unsupervised segmentation model designed for extracting
main blood vessels from Optical Coherence Tomography Angiography (OCTA) images.
The process begins with the Space Colonization Algorithm (SCA) to rapidly
generate a skeleton of vessels, featuring their radii. By synergistically
integrating generative adversarial networks (GANs) with biostatistical modeling
of vessel radii, X-GAN enables a fast reconstruction of both 2D and 3D
representations of the vessels. Based on this reconstruction, X-GAN achieves
nearly 100\% segmentation accuracy without relying on labeled data or
high-performance computing resources. Also, to address the Issue, data scarity,
we introduce GSS-RetVein, a high-definition mixed 2D and 3D glaucoma retinal
dataset. GSS-RetVein provides a rigorous benchmark due to its exceptionally
clear capillary structures, introducing controlled noise for testing model
robustness. Its 2D images feature sharp capillary boundaries, while its 3D
component enhances vascular reconstruction and blood flow prediction,
supporting glaucoma progression simulations. Experimental results confirm
GSS-RetVein's superiority in evaluating main vessel segmentation compared to
existing datasets. Code and dataset are here:
https://github.com/VikiXie/SatMar8.
