SS-SwinUnet: A Distillation Method of Swin Transformer for Superior Ocular Image Segmentation

Precise segmentation of the pupil, iris, and sclera is critical for diagnosing and treating ocular diseases such as glaucoma, strabismus, and retinal disorders. However, the fine structural differences within the eye and the interference of complex backgrounds, especially with VR devices prone to reflections, tilts, distortions, and occlusions, present significant challenges. In this paper, we introduce SS-SwinUnet, a novel segmentation method that integrates Swin Transformer and knowledge distillation to achieve superior performance. Specifically, SS-SwinUnet balances feature transfer between the encoder and decoder, reducing redundancy and enhancing representation. Additionally, we incorporate a Boundary Difference over Union Loss to improve boundary segmentation accuracy. We also propose an eye modeling method that parameterizes segmentation results to optimize the semantic segmentation of ocular structures. We constructed the TongRenD dataset, comprising 400 VR-captured videos and 4,100 images, which, along with the TEyeD dataset, was used in our experiments. Results demonstrate that SS-SwinUnet significantly outperforms existing medical image segmentation methods across multiple datasets.