Brain extraction is a critical preprocessing step in almost every neuroimaging study, enabling accurate segmentation and analysis of Magnetic Resonance Imaging (MRI) data. FSL's Brain Extraction Tool (BET), although considered the current gold standard, presents limitations such as over-extraction, which can be particularly problematic in brains with lesions affecting the outer regions, 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 across a wide range of applications. In this paper, we present a comparative analysis of brain extraction techniques using BET and SAM on a variety of brain scans with varying image qualities, MRI sequences, and brain lesions affecting different brain regions. We find that SAM outperforms BET based on several 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. These results suggest that SAM has the potential to emerge as a more accurate and precise tool for a broad range of brain extraction applications.

翻译：脑提取是几乎每项神经影像学研究中的关键预处理步骤，它能实现对磁共振成像（MRI）数据的精确分割与分析。FSL的脑提取工具（BET）虽然被认为是当前的金标准，但仍存在局限性，例如过度提取（尤其在处理影响外脑区域的病变脑部时尤为突出）、脑组织与周围脑膜区分不准确，以及对图像质量敏感等问题。近期计算机视觉研究的进展催生了Meta AI开发的Segment Anything Model（SAM），该模型在广泛的应用中展现出巨大潜力。本文针对不同图像质量、MRI序列及影响不同脑区的病变脑部扫描，对比分析了采用BET与SAM的脑提取技术。研究发现，基于多项指标，SAM在多数情况下优于BET，尤其在图像受信号不均匀性、非各向同性体素分辨率或涉及外脑区域及脑膜附近病变影响时表现更佳。这些结果表明，SAM有望成为更精确、更精准的脑提取工具，适用于广泛的应用场景。