Segment Anything Model for Brain Tumor Segmentation
This work addresses brain tumor segmentation for clinical diagnosis, but it is incremental as it tests an existing model on a new medical dataset.
The study applied the Segment Anything Model (SAM) to brain tumor segmentation and found that without fine-tuning, it underperformed compared to the current state-of-the-art model.
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.