SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective

Understand

Segment Anything Model (SAM) is a foundation model for semantic segmentation and shows excellent generalization capability with the prompts.

• In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs.
• unprompted; (ii) bounding box vs.
• points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs.