X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening
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Abstract (arXiv)
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 the generative adversarial network (GAN) 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. Experimental results confirm X-GAN's superiority in evaluating main vessel segmentation compared to existing deep learning models.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 61 references that are in the map. First posted on arXiv 9 Mar 2025.
Builds on
28 of 61 references.
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16 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.