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

Understand

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.