Enhancing Corneal Endothelial Cell Image Segmentation via Generative Data Augmentation and Transfer Learning.

Corneal endothelium segmentation is crucial for clinical evaluation, but remains constrained by two significant challenges: the scarcity of high-quality annotations and the difficulty of reliably delineating complex pathological patterns. In particular, the corneal guttata exhibits high structural variability, resulting in limited and inconsistent training data. Previous studies rely heavily on manual labeling or GAN-based synthesis, which cannot generate text-controlled paired image-mask datasets at scale and lack the required fidelity for pretraining modern segmentation models. To overcome these limitations, this study introduces a two-stage training framework that integrates text-guided diffusion models, transfer learning, and boundary-refinement algorithms. A Stable Diffusion model is fine-tuned with LoRA and conditioned with ControlNet to generate three categories of synthetic data: endothelial images with guttata, without guttata, and their corresponding binary boundary masks. U-Net, TransUnet, and SwinUnet are then pretrained on synthetic paired datasets and fine-tuned on limited real annotations, with a watershed algorithm incorporated as a post-processing step to enhance boundary continuity in the final segmentation outputs. Experimental results demonstrate that increasing the size of synthetic pretraining data generally improves segmentation accuracy. Among the models evaluated, U-Net achieves the highest segmentation performance, indicating that local feature extraction is particularly well suited to corneal endothelial morphology compared with global self-attention mechanisms. Although watershed post-processing may slightly lower Dice scores due to its sensitivity to overlap metrics, it offers a clinically meaningful trade-off by substantially improving boundary continuity and yielding more anatomically coherent cell borders.