  The recent Segment Anything Model (SAM) 2 has demonstrated remarkable
foundational competence in semantic segmentation, with its memory mechanism and
mask decoder further addressing challenges in video tracking and object
occlusion, thereby achieving superior results in interactive segmentation for
both images and videos. Building upon our previous empirical studies, we
further explore the zero-shot segmentation performance of SAM 2 in
robot-assisted surgery based on prompts, alongside its robustness against
real-world corruption. For static images, we employ two forms of prompts:
1-point and bounding box, while for video sequences, the 1-point prompt is
applied to the initial frame. Through extensive experimentation on the MICCAI
EndoVis 2017 and EndoVis 2018 benchmarks, SAM 2, when utilizing bounding box
prompts, outperforms state-of-the-art (SOTA) methods in comparative
evaluations. The results with point prompts also exhibit a substantial
enhancement over SAM's capabilities, nearing or even surpassing existing
unprompted SOTA methodologies. Besides, SAM 2 demonstrates improved inference
speed and less performance degradation against various image corruption.
Although slightly unsatisfactory results remain in specific edges or regions,
SAM 2's robust adaptability to 1-point prompts underscores its potential for
downstream surgical tasks with limited prompt requirements.
