Segment Anything Model for Brain Tumor Segmentation

TL;DR

This paper evaluates the Segment Anything Model's ability to segment brain tumors, highlighting its zero-shot capabilities and the performance gap compared to specialized models without fine-tuning.

Contribution

The study applies SAM to brain tumor segmentation and assesses its zero-shot performance, revealing limitations and potential for further improvement.

Findings

SAM performs reasonably without fine-tuning

Significant gap remains compared to SOTA models

Zero-shot generalization has potential but needs enhancement

Abstract

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.