用大模型提升脑图谱分割精度，95个区域精准划分。
BrainSegNet: A Novel Framework for Whole-Brain MRI Parcellation Enhanced by Large Models
- 融合U-Net与SAM结构，改进编码器和解码器以增强细节捕捉。
- 在HCP数据集上达到更高准确率，复杂多标签分割更鲁棒。
- 适合神经影像分析、脑图谱构建等需要高精度分割的研究者。
基于MRI的全脑分区是一项关键但极具挑战的任务，因其需将大脑划分为众多小而形状不规则的区域。传统模板配准方法已逐渐被深度学习取代以提高效率。尽管像分割任意模型（SAM）这样的大模型具备可迁移的特征表示能力，但其并未针对脑区分割所需的高精度进行优化。为此，我们提出BrainSegNet框架，通过引入U-Net跳跃连接和专用模块对SAM进行改进，实现对95个脑区的高精度全脑分割。核心组件包括：结合U-Net跳跃连接与SAM变压器块的混合编码器、采用金字塔池化处理不同尺寸结构的多尺度注意力解码器，以及用于锐化边界的边界细化模块。在人类连接组计划（HCP）数据集上的实验表明，BrainSegNet显著优于多个先进方法，在复杂多标签分割任务中展现出更高的准确率和鲁棒性。
原文摘要 · Abstract (English)
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.
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