Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review.

翻译：局灶性皮质发育不良（FCD）是药物难治性局灶性癫痫的主要病因，可通过手术治愈。这些病变极其细微，甚至经验丰富的神经放射科医师也常会漏诊。因此，“金标准”的人工病变掩膜成本高昂、数量有限，且评估者间存在较大差异。现有FCD检测方法受限于基于顶点或分块的处理方式，缺乏全脑全局上下文信息，导致假阳性预测过多。本文提出将问题转化为基于图卷积网络（GCN）的语义分割任务，使模型能够学习脑区间的空间关系。为应对FCD识别的特殊挑战，我们设计的模型包含辅助损失函数以预测病灶距离（减少假阳性），以及弱监督分类损失函数（从不确定性病变掩膜中学习）。在包含1015名受试者的多中心数据集中，基于表面特征和结构性MRI数据的人工病变掩膜，所提出的GCN获得了0.74的AUC，较此前使用的逐顶点多层感知机（MLP）分类器（AUC 0.64）有显著提升。当敏感度阈值设为67%时，GCN的特异性达71%，而MLP仅为49%。这一特异性提升对于将病变检测工具整合至放射学工作流程至关重要，可增强临床对人工智能辅助放射学工具的信任，并减少需要专家审查的区域数量。