# SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning

arXiv: https://arxiv.org/abs/2304.04738 · HTML: https://mlsanity.com/paper/2304.04738/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)

## Built on by (most cited first, 24 of 24)

- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Matte Anything: Interactive Natural Image Matting with Segment Anything Models](https://mlsanity.com/paper/2306.04121/) (2023)
- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.03678/) (2023)
- [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024)
- [Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation](https://mlsanity.com/paper/2502.00630/) (2025)
- [Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification](https://mlsanity.com/paper/2311.10529/) (2023)
- [Recent Advances in Medical Imaging Segmentation: A Survey](https://mlsanity.com/paper/2505.09274/) (2025)
- [Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?](https://mlsanity.com/paper/2409.07960/) (2024)
- [Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network](https://mlsanity.com/paper/2411.13873/) (2024)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2403.14103/) (2024)
- [RadioActive: 3D Radiological Interactive Segmentation Benchmark](https://mlsanity.com/paper/2411.07885/) (2024)
- [BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision](https://mlsanity.com/paper/2311.17960/) (2023)
- [SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction](https://mlsanity.com/paper/2406.00663/) (2024)
- [Segment Anything for Dendrites from Electron Microscopy](https://mlsanity.com/paper/2411.02562/) (2024)
- [MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention](https://mlsanity.com/paper/2501.03839/) (2025)
- [Skull stripping with purely synthetic data](https://mlsanity.com/paper/2505.07159/) (2025)
- [Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation](https://mlsanity.com/paper/2505.22105/) (2025)
- [Reinforcing the Weakest Links: Modernizing SIENA with Targeted Deep Learning Integration](https://mlsanity.com/paper/2603.12951/) (2026)

## Abstract (arXiv)

Brain extraction is a critical preprocessing step in various neuroimaging studies, particularly enabling accurate separation of brain from non-brain tissue and segmentation of relevant within-brain tissue compartments and structures using Magnetic Resonance Imaging (MRI) data. FSL's Brain Extraction Tool (BET), although considered the current gold standard for automatic brain extraction, presents limitations and can lead to errors such as over-extraction in brains with lesions affecting the outer parts of the brain, inaccurate differentiation between brain tissue and surrounding meninges, and susceptibility to image quality issues. Recent advances in computer vision research have led to the development of the Segment Anything Model (SAM) by Meta AI, which has demonstrated remarkable potential in zero-shot segmentation of objects in real-world scenarios. In the current paper, we present a comparative analysis of brain extraction techniques comparing SAM with a widely used and current gold standard technique called BET on a variety of brain scans with varying image qualities, MR sequences, and brain lesions affecting different brain regions. We find that SAM outperforms BET based on average Dice coefficient, IoU and accuracy metrics, particularly in cases where image quality is compromised by signal inhomogeneities, non-isotropic voxel resolutions, or the presence of brain lesions that are located near (or involve) the outer regions of the brain and the meninges. In addition, SAM has also unsurpassed segmentation properties allowing a fine grain separation of different issue compartments and different brain structures. These results suggest that SAM has the potential to emerge as a more accurate, robust and versatile tool for a broad range of brain extraction and segmentation applications.