  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.
