The traditional Segment Anything Model （SAM） relies on manual prompts during segmentation of Meibomian gland images， making it difficult to handle issues such as dense glands， irregular shapes， and blurred boundaries. To address this， an improved model， namely ResSAM， was proposed. ResSAM eliminated the reliance on manual intervention by introducing an automatic prompt encoder. The backbone network was pruned and optimized to further enhance the model's segmentation efficiency. Focal Loss and Smooth IoU Loss were used for training optimization， and the SE （Squeeze-and-Excitation） and cross-attention mechanisms were integrated to reduce the impact of individual differences and blurred boundaries， thereby improving the model's segmentation accuracy. Experimental results on two self-built datasets， Lower Lid and Upper Lid， showed that ResSAM achieved the best performance in terms of the number of parameters and Giga FLoating-point OPerations （GFLOPs）； its segmentation results obtained the highest Dice scores （88.69% and 87.75%， respectively） and the highest Intersection-over-Union （IoU） values （79.69% and 78.58%， respectively）. The research results indicate that the ResSAM optimizes both efficiency and accuracy， supporting early prevention and clinical diagnosis of Meibomian Gland Dysfunction （MGD）.
