CRIL-U-Net：用于从T1w和FLAIR MRI中分割局灶性皮质发育不良的紧凑比率交互学习网络
CRIL-U-Net: Compact Ratio-Interaction Learning for Focal Cortical Dysplasia Segmentation from T1w and FLAIR MRI
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中文总结 AI 辅助
该研究针对II型局灶性皮质发育不良MRI自动分割难题，提出CRIL-U-Net模型，在85例患者与25例对照的五折交叉验证中，其性能显著优于传统及注意力型U-Net。
中文摘要 AI 辅助
II型局灶性皮质发育不良（FCD）是耐药性局灶性癫痫的重要结构性病因，但其体积小、外观异质性强且MRI特征微妙，使得自动分割极具挑战性。传统多模态网络通常将T1加权（T1w）图像与液体衰减反转恢复（FLAIR）图像进行拼接，要求后续层隐式学习有用的跨模态关系。我们提出CRIL-U-Net，一种3D U-Net，其集成了紧凑比率交互学习模块，该模块结合了局部空间特征、体素级跨模态混合以及受双向比率启发的交互作用。我们在85例FCD患者和25例健康对照者上采用五折交叉验证，将CRIL-U-Net与传统3D U-Net及输入自注意力U-Net进行对比。每种架构均使用Dice-二元交叉熵（Dice-BCE）和焦点Tversky-焦点（FTF）损失进行独立训练。在使用FTF损失时，CRIL-U-Net的平均Dice分数为0.196±0.262，达到最高水平，而U-Net的分数为0.136±0.224，注意力对比模型的分数为0.135±0.214。它在85例病例中的44例产生了非零病变重叠，而U-Net为36例。在FTF损失下，经错误发现率校正后，CRIL-U-Net显著优于两种对比架构。这些发现表明，在结合不平衡感知目标的受控U-Net设置中，紧凑跨模态表示学习可改善FCD分割，尽管剩余48.2%的零重叠率凸显了进一步验证和方法学开发的必要性。
英文摘要
Focal cortical dysplasia (FCD) type II is an important structural cause of drug-resistant focal epilepsy, but its small size, heterogeneous appearance, and subtle MRI characteristics make automated segmentation challenging. Conventional multimodal networks commonly concatenate T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images, requiring subsequent layers to learn useful cross-modal relationships implicitly. We propose CRIL-U-Net, a 3D U-Net incorporating a Compact Ratio-Interaction Learning module that combines local spatial features, voxel-wise cross-modal mixing, and bidirectional ratio-inspired interactions. CRIL-U-Net was compared with a conventional 3D U-Net and an input self-attention U-Net using five-fold cross-validation on 85 FCD subjects and 25 healthy controls. Each architecture was trained independently using Dice-binary cross-entropy (Dice-BCE) and Focal Tversky-Focal (FTF) losses. With FTF, CRIL-U-Net achieved the highest mean Dice score (0.196 +/- 0.262), compared with 0.136 +/- 0.224 for the U-Net and 0.135 +/- 0.214 for the attention comparator. It produced nonzero lesion overlap in 44 of 85 cases, compared with 36 for the U-Net. Under FTF, CRIL-U-Net significantly outperformed both comparison architectures after false-discovery-rate correction. These findings suggest that compact cross-modal representation learning can improve FCD segmentation within a controlled U-Net setting when combined with an imbalance-aware objective, although the remaining zero-overlap rate of 48.2% highlights the need for further validation and methodological development.
