Medical image segmentation and video object segmentation are essential for diagnosing and analyzing diseases by identifying and measuring biological structures. Recent advances in natural domain have been driven by foundation models like the Segment Anything Model 2 (SAM 2). To explore the performance of SAM 2 in biomedical applications, we designed two evaluation pipelines for single-frame image segmentation and multi-frame video segmentation with varied prompt designs, revealing SAM 2's limitations in medical contexts. Consequently, we developed BioSAM 2, an enhanced foundation model optimized for biomedical data based on SAM 2. Our experiments show that BioSAM 2 not only surpasses the performance of existing state-of-the-art foundation models but also matches or even exceeds specialist models, demonstrating its efficacy and potential in the medical domain.

本研究解决了生物医学图像和视频分割的挑战，特别是在医疗背景下的应用。我们提出了BioSAM 2，这是一种基于SAM 2的优化模型，通过实验结果显示其在性能上超越了现有最先进模型，并且与专业模型相匹配甚至更优，展示了其在医疗领域的巨大潜力。

生物医学SAM 2：在生物医学图像和视频中分割任何内容