## 3 Methods
### Prompt Selection  
SAM offers three primary segmentation modes: segment everything mode, bounding box mode, and point mode. However, each of these modes exhibits distinct characteristics, leading to certain biases in their segmentation outcomes. [PERSON] et al. [9] explored the efficacy of three distinct segmentation approaches on standard abdominal CT scans and proved that the bounding box mode has broader practical value than the segment everything mode and point mode.  
Considering the significant differences in target morphology, image format, and target region features between abdominal organ images and colonoscopy polyp images, we apply SAM with these three segmentation modes to colonoscopy polyp data to investigate whether the bounding box mode is the optimal choice for our segmentation task. Experimental results are shown in Figure 1. **(1) Segment everything mode:** This mode partitions the entire image into multiple regions based on image intensity by defining segmentation grid points (here using 9 \(\times\) 9), but the segmentation results often lack semantic labels and cannot clearly focus on the ROI that clinical doctors care about, limiting its utility. In Figure 1c, it can be seen that, due to indistinguishable features between polyps and surrounding skin tissue, it is easy to regard the entire polyp area as background in this mode. **(2) Bounding box mode:** This mode segments the target objects by providing points for the lower right corner and upper left corner of the bounding box (in green in Figure 2d). In Figure 1d, it can be seen that this mode can better identify the polyp area and is more similar to the ground truth in Figure 1b. **(3) Point mode:** This mode generates the required mask by continuously adding foreground or background points. In Figure 1e1-e4, it can be seen that the segmented area with one point-prompt under this mode will change with the position of the point, leading to segmentation instability, and the contour of the target area does not converge. The target area will only approach the ground truth after repeatedly adding foreground and background points. In which, the green represents the foreground points, and the red represents the background points. Overall, the segmentation results with multiple point-prompts in Figure 1f1-f4 under the point mode are not as good as that in the box mode in Figure 1d. The point mode usually requires multiple prediction corrections and iterations, which is consistent with the conclusions of the study [9].  
In summary, when applying SAM to colonoscopy polyp segmentation, the segment everything mode often leads to irrelevant region divisions, while the point mode's ambiguity often requires several rounds of adjustments for accurate predictions. Conversely, the bounding box mode precisely delineates the region of interest (ROI), yielding satisfactory segmentation outcomes with fewer iterative corrections. Therefore, in subsequent experiments, bounding boxes are utilized as prompt information for both pseudo-label generation and fine-tuning in the SAM model.  
Figure 1: Segmentation results of SAM on a colonoscopy polyp image for different segmentation modes.