Whole-brain parcellation from MRI is a critical yet challenging task due to the complexity of subdividing the brain into numerous small, irregular shaped regions. Traditionally, template-registration methods were used, but recent advances have shifted to deep learning for faster workflows. While large models like the Segment Anything Model (SAM) offer transferable feature representations, they are not tailored for the high precision required in brain parcellation. To address this, we propose BrainSegNet, a novel framework that adapts SAM for accurate whole-brain parcellation into 95 regions. We enhance SAM by integrating U-Net skip connections and specialized modules into its encoder and decoder, enabling fine-grained anatomical precision. Key components include a hybrid encoder combining U-Net skip connections with SAM's transformer blocks, a multi-scale attention decoder with pyramid pooling for varying-sized structures, and a boundary refinement module to sharpen edges. Experimental results on the Human Connectome Project (HCP) dataset demonstrate that BrainSegNet outperforms several state-of-the-art methods, achieving higher accuracy and robustness in complex, multi-label parcellation.

翻译：全脑MRI分区是一项关键但极具挑战性的任务，原因在于将大脑细分为众多形状不规则的小区域具有高度复杂性。传统方法多采用模板配准技术，而近期研究趋势已转向深度学习以实现更快速的工作流程。尽管如Segment Anything Model (SAM) 等大模型提供了可迁移的特征表示，但它们并未针对脑分区所需的高精度进行专门设计。为此，我们提出BrainSegNet——一种将SAM适配用于精确划分95个全脑区域的新型框架。我们通过将U-Net跳跃连接及专用模块集成到SAM的编码器与解码器中来增强其性能，从而实现细粒度的解剖学精度。核心组件包括：融合U-Net跳跃连接与SAM Transformer模块的混合编码器、采用金字塔池化处理多尺度结构的注意力解码器，以及用于锐化边界的轮廓优化模块。在人类连接组计划（HCP）数据集上的实验结果表明，BrainSegNet在复杂多标签分区任务中优于多种先进方法，实现了更高的准确性与鲁棒性。