Semi-supervised Focal Cortical Dysplasia Lesion Segmentation with Dual Branch Consistency Regularization

Focal cortical dysplasia (FCD) is a significant cause of intractable epilepsy, with its identification and segmentation being crucial for diagnosis. While fully-supervised learning models can detect and segment FCD, they are limited by the need for extensive labeled data, which is time-consuming and requires expert annotation. To address this, we introduce a semi-supervised model for FCD segmentation, leveraging a small set of labeled data alongside unlabeled data. Our method estimates uncertainty by measuring voxel-wise deviations between predictions and ground truth, using both ground truth and pseudo-labels to refine segmentation masks. For unlabeled data, multi-scale predictions capture different frequency components, allowing for selective consistency regularization based on region reliability. This approach is the first application of semi-supervised learning in epilepsy segmentation and demonstrates superior performance over state-of-the-art methods in experiments on clinical and public datasets.