We introduce a new technique for generating retinal fundus images that have anatomically accurate vascular structures, using diffusion models. We generate artery/vein masks to create the vascular structure, which we then condition to produce retinal fundus images. The proposed method can generate high-quality images with more realistic vascular structures and can create a diverse range of images based on the strengths of the diffusion model. We present quantitative evaluations that demonstrate the performance improvement using our method for data augmentation on vessel segmentation and artery/vein classification. We also present Turing test results by clinical experts, showing that our generated images are difficult to distinguish with real images. We believe that our method can be applied to construct stand-alone datasets that are irrelevant of patient privacy.

翻译：我们提出了一种利用扩散模型生成具有解剖精确血管结构的视网膜眼底图像的新技术。通过生成动脉/静脉掩模来构建血管结构，并以此作为条件生成视网膜眼底图像。该方法能够生成具有更逼真血管结构的高质量图像，并基于扩散模型的优势生成多样化的图像。量化评估表明，该方法在血管分割和动脉/静脉分类的数据增强中显著提升了性能。临床专家的图灵测试结果显示，我们生成的图像与真实图像难以区分。我们相信，该方法可用于构建与患者隐私无关的独立数据集。