  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.
