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：无需大规模样本进行分割学习是人类与生俱来的能力。近年来，Segment Anything Model（SAM）在零样本图像分割方面表现突出，引起了计算机视觉领域的广泛关注。在本文中，我们探究了 SAM 在医学图像分析方面的能力，尤其是在多相肝肿瘤分割（MPLiTS）方面的表现，涉及提示、数据分辨率和不同阶段。实验结果表明，SAM 与预期性能之间可能存在较大差距。值得欣慰的是，定性结果显示，SAM 是交互式医学图像分割社区的强大注释工具。
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