Text Guidance and Distance Gating Improve Detection and Segmentation of Focal Cortical Dysplasia

Focal Cortical Dysplasia ({FCD}) is a leading cause of drug-resistant focal epilepsy, and its identification on Magnetic Resonance Imaging ({MRI}) can enable potentially curative surgical treatment. {FCD} features are often subtle and heterogeneous and current deep learning models for automated detection exhibit limited sensitivity. In clinical workflows, prior knowledge about the suspected localization of seizure onset can guide lesion search. We hypothesize that this prior knowledge can similarly guide a deep learning model in the presence of ambiguous imaging features. We propose a text-guided graph neural network that incorporates prior knowledge to guide {FCD} detection on cortical features derived from {MRI}. We further introduce a cluster-wise distance-based gating mechanism that leverages the per-vertex distance regression head at inference to suppress false-positive predictions. Experimental results demonstrate that hemisphere- and lobe-level localization information improves sensitivity and segmentation quality, while the gating mechanism substantially increases specificity. Code is available in a {GitHub} repository.