Enhanced Corneal Endothelial Cell Segmentation via Frequency-Selected Residual Fourier Diffusion Models

Segmenting corneal endothelial cells in conditions like Fuchs endothelial dystrophy (FED) is challenging due to guttae obscuring cell details and complicating imaging. This is further compounded by labor-intensive manual annotations and a lack of large annotated datasets. To address these issues, we introduce a novel two-stage framework using Denoising Diffusion Probabilistic Models (DDPMs) for generating training pairs of corneal endothelial cell images. In the first stage, we generate synthetic endothelial labels, which are then used to guide the production of high-resolution corneal images in the second stage. We also present the Fourier Residual Block with Frequency Selection (FRB-FS), which enhances important high-frequency details for clearer textures and edges, while suppressing irrelevant low-frequency components. This is the first application of diffusion models to corneal endothelial cell segmentation. Extensive experiments and ablation studies on two benchmark datasets demonstrate the effectiveness of our framework.