# Segment Anything Model for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2309.08434 · HTML: https://mlsanity.com/paper/2309.08434/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation](https://mlsanity.com/paper/2206.02425/) (2022)
- [INTERN: A New Learning Paradigm Towards General Vision](https://mlsanity.com/paper/2111.08687/) (2021)
- [SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM](https://mlsanity.com/paper/2304.05622/) (2023)

## Built on by (most cited first, 4 of 4)

- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models](https://mlsanity.com/paper/2503.06816/) (2025)
- [Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection](https://mlsanity.com/paper/2408.14847/) (2024)

## Abstract (arXiv)

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.