When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
This work addresses medical image segmentation for liver tumors, but it is incremental as it applies an existing model to a new domain without major improvements.
The study investigated the Segment Anything Model (SAM) for multi-phase liver tumor segmentation and found a large performance gap compared to expectations, though it identified SAM as a useful annotation tool for interactive medical image segmentation.
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.