  Glioma is a prevalent brain tumor that poses a significant health risk to
individuals. Accurate segmentation of brain tumor is essential for clinical
diagnosis and treatment. The Segment Anything Model(SAM), released by Meta AI,
is a fundamental model in image segmentation and has excellent zero-sample
generalization capabilities. Thus, it is interesting to apply SAM to the task
of brain tumor segmentation. In this study, we evaluated the performance of SAM
on brain tumor segmentation and found that without any model fine-tuning, there
is still a gap between SAM and the current state-of-the-art(SOTA) model.
