用信息分解选最有效的脑肿瘤MRI组合，省资源还高效
Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
- 用部分信息分解法评估多序列MRI的冗余、独有和协同信息
- 选出T1c+T2-FLAIR组合，分割效果接近全序列（Dice 0.676）
- 适合计算资源有限但需高精度分割的医学影像研究者
多对比度3D MRI分割在使用所有序列时计算成本高。我们评估了一种基于预训练部分信息分解（PID）的框架，该框架根据输入对关于肿瘤负荷的冗余、独特和协同信息进行排序，并选择排名最高的组合用于下游训练。在T1n、T1c、T2w和T2-FLAIR MRI序列上，该框架优选出T1c+T2-FLAIR。随后我们训练了十一组结构相同的轻量级3D U-Net，采用不同输入配置。在独立测试集上，T1c+T2-FLAIR为最优双输入配置，平均Dice系数达0.676（全四序列为0.687）。对全输入模型的独立Shapley分析也表明，T2-FLAIR和T1c是影响最大的输入，其交互作用最强。结果证明，基于PID的预训练选择策略能有效识别紧凑且信息丰富的MRI输入集，避免昂贵的3D模型开发。
原文摘要 · Abstract (English)
Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework that ranks input pairs according to their redundant, unique, and synergistic information about regional tumor burden and selects the highest-ranked pair for downstream training. Applied to T1n, T1c, T2w, and T2-FLAIR MRI, the framework selected T1c+T2-FLAIR. We then trained eleven architecturally identical lightweight 3D U-Nets using different input configurations. On an independent test cohort, T1c+T2-FLAIR was the strongest two-input configuration and ranked second overall in mean Dice (0.676 versus 0.687 for all four inputs). Independent Shapley analysis on the full-input model also identified T2-FLAIR and T1c as the most influential inputs and their pairwise interaction as the strongest. These findings demonstrate the practical value of PID based pre-training selection for identifying compact, informative MRI input sets before costly 3D model development.
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