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原文摘要

Focal cortical dysplasia (FCD) type II is an important cause of drug-resistant focal epilepsy, but its small size and subtle magnetic resonance imaging (MRI) appearance make automated segmentation difficult. Conventional multimodal networks concatenate T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images and rely on subsequent layers to learn their relationships implicitly. We propose Compact Ratio-Interaction Learning U-Net (CRIL-U-Net), a 3D U-Net extension 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 3D U-Net and a U-Net with input-level self-attention using five-fold cross-validation on 85 FCD subjects and 25 healthy controls. Each architecture was trained independently with Dice-binary cross-entropy (Dice-BCE) and Focal Tversky-Focal (FTF) losses. With FTF, CRIL-U-Net achieved the highest mean Dice score ($0.196\pm0.262$), compared with $0.136\pm0.224$ for the U-Net and $0.135\pm0.214$ for the attention comparator. It localised 44 of 85 lesions, compared with 36 for the U-Net, and significantly outperformed both comparison architectures after false-discovery-rate correction. CRIL adds only 4,320 parameters to the backbone, indicating that compact cross-modal representation learning can improve FCD segmentation when combined with an imbalance-aware objective. The remaining 48.2\% miss rate nevertheless requires further methodological development and external validation.