X-GAN无需标注数据，快速精准分割青光眼筛查中的视网膜主血管。
X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening
- 结合生成对抗网络与生物统计建模，利用空间占领算法快速生成血管骨架。
- 在OCTA图像上实现接近100%的分割准确率，无需标签和高性能计算。
- 适合医疗影像分析、无监督学习研究者，尤其关注青光眼早期检测人群。
视网膜主血管的结构变化是青光眼发生与进展的关键生物标志物。准确识别这些血管对血管建模至关重要，但极具挑战性。本文提出X-GAN，一种基于生成式AI的无监督分割模型，用于从光学相干断层扫描血管成像（OCTA）图像中提取主血管。该方法首先利用空间占领算法（SCA）快速生成包含血管半径信息的骨架；再通过将生成对抗网络（GAN）与血管半径的生物统计建模相结合，实现2D与3D血管结构的高效重建。基于此，X-GAN在无需标注数据或高性能计算资源的条件下，达到近100%的分割准确率。实验结果表明，其在主血管分割性能上优于现有深度学习模型。
原文摘要 · Abstract (English)
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.
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