Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this work, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings. We hope this report will provide insights to advance the field of polyp segmentation and promote more interesting work in the future. This project is publicly available at https://github.com/ sajjad-sh33/Polyp-SAM-2.

翻译：息肉分割在结直肠癌的早期检测与诊断中起着至关重要的作用。然而，获取精确的分割结果通常需要耗费大量人力的标注工作以及专用模型。近期，Meta AI Research 发布了一个通用的 Segment Anything Model 2 (SAM 2)，该模型在多项分割任务中已展现出有前景的性能。在本工作中，我们评估了 SAM 2 在不同提示设置下分割息肉的表现。我们希望本报告能为推进息肉分割领域的发展提供见解，并促进未来更多有意义的工作。本项目已在 https://github.com/sajjad-sh33/Polyp-SAM-2 公开。