用图神经网络解决不同数据集间脑区分割的差异问题
GDAIP: A Graph-Based Domain Adaptive Framework for Individual Brain Parcellation
- 构建跨数据集的群体与个体脑图，利用图注意力网络自适应调整
- 在跨会话场景下达到0.81以上的骰子系数，边界拓扑合理
- 适合需要跨数据集精准脑区分割的研究者使用
近期深度学习方法在从功能磁共振成像（fMRI）中学习个体脑区分割方面展现出潜力。然而，大多数现有方法假设域间数据分布一致，难以应对真实世界跨数据集场景中的域偏移问题。为此，我们提出图域自适应脑区分割框架GDAIP，将图注意力网络（GAT）与基于最小最大熵（MME）的域自适应相结合。我们在群体和个体层面构建跨数据集脑图，通过半监督训练及对目标脑图未标记节点预测熵的对抗优化，实现参考图谱从群体级脑图到个体脑图的自适应迁移，从而在跨数据集条件下完成个体脑区分割。我们通过分割可视化、骰子系数和功能同质性进行评估。实验结果表明，GDAIP生成的个体脑区分割具有合理的拓扑边界、强跨会话一致性，并能有效反映功能组织特性。
原文摘要 · Abstract (English)
Recent deep learning approaches have shown promise in learning such individual brain parcellations from functional magnetic resonance imaging (fMRI). However, most existing methods assume consistent data distributions across domains and struggle with domain shifts inherent to real-world cross-dataset scenarios. To address this challenge, we proposed Graph Domain Adaptation for Individual Parcellation (GDAIP), a novel framework that integrates Graph Attention Networks (GAT) with Minimax Entropy (MME)-based domain adaptation. We construct cross-dataset brain graphs at both the group and individual levels. By leveraging semi-supervised training and adversarial optimization of the prediction entropy on unlabeled vertices from target brain graph, the reference atlas is adapted from the group-level brain graph to the individual brain graph, enabling individual parcellation under cross-dataset settings. We evaluated our method using parcellation visualization, Dice coefficient, and functional homogeneity. Experimental results demonstrate that GDAIP produces individual parcellations with topologically plausible boundaries, strong cross-session consistency, and ability of reflecting functional organization.
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