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

When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation