Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation (MPLiTS), in terms of prompts, data resolution, phases. Experimental results demonstrate that there might be a large gap between SAM and expected performance. Fortunately, the qualitative results show that SAM is a powerful annotation tool for the community of interactive medical image segmentation.

翻译：人类无需大规模样本即可学习分割，这是其固有本领。近期，分割一切模型（SAM）展现出显著的零样本图像分割能力，引起了计算机视觉领域的广泛关注。本文从提示信息、数据分辨率、扫描期相三个维度，探究了SAM在医学图像分析中的能力，尤其聚焦于多期肝脏肿瘤分割（MPLiTS）。实验结果表明，SAM的表现与预期性能之间可能存在较大差距。幸运的是，定性结果表明SAM是交互式医学图像分割领域一款强大的标注工具。