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.

翻译：胶质瘤是一种常见的脑肿瘤，对个体健康构成重大风险。脑肿瘤的精确分割对于临床诊断和治疗至关重要。Meta AI发布的Segment Anything模型（SAM）是图像分割领域的基础模型，具有卓越的零样本泛化能力。因此，将SAM应用于脑肿瘤分割任务具有重要意义。在本研究中，我们评估了SAM在脑肿瘤分割上的性能，发现未经任何模型微调的情况下，SAM与当前最先进的（SOTA）模型之间仍存在差距。