CAVE-OCTA: A Vessel Expressions Controlled Diffusion Framework for Synthetic OCTA Image Generation

Optical coherence tomography angiography (OCTA) is a key technique for diagnosing retinal diseases. It clearly scans the detailed retinal vessel distribution, density, and other key biomarkers onto the images. Collecting and annotating high-quality OCTA data is challenging and requires significant manual costs. This paper proposes a fine-tuning framework called CAVE-OCTA (Controlled According to the Vessel Expressions) for generating synthetic en-face OCTA samples. It fine-tunes the stable diffusion model and controls the vascular distribution of the generated images through diversified retinal vessel expressions. We quantitatively evaluated the synthetic image quality and proved it to improve segmentation results on existing models. Additionally, we provided methods for creating several control prompt images. The code is available at https://github.com/ShellRedia/CAVE-OCTA.