Benchmarking MRI Representations for Deep Learning-Based Focal Cortical Dysplasia Segmentation

2026-07-16

AI Summary

Focal cortical dysplasia (FCD) presents significant challenges for automated segmentation on conventional MRI due to its subtle and highly heterogeneous imaging characteristics. This study systematically evaluates the impact of various MRI representations—including T1-weighted (T1w), FLAIR, their ratio-derived images, and multimodal combinations—on FCD segmentation performance within the nnU-Net framework. Employing standardized preprocessing and five-fold cross-validation ensures a fair comparison across configurations. The work reveals, for the first time, the critical influence of MRI representation design on segmentation efficacy, demonstrating that a four-channel multimodal input substantially outperforms conventional dual-modality approaches, achieving a Dice coefficient of 0.376—a relative improvement of 5.0%. These findings underscore the effectiveness and potential of optimized multimodal MRI representations for FCD segmentation.

Abstract

Focal cortical dysplasia (FCD) is one of the leading structural causes of drug-resistant focal epilepsy, yet its subtle and heterogeneous imaging characteristics make accurate identification and delineation challenging on conventional magnetic resonance imaging (MRI). Although T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images are routinely acquired for presurgical evaluation, the contribution of different MRI representations to deep learning-based FCD segmentation remains poorly understood. In this study, we present a systematic benchmark of MRI representations for automated FCD segmentation using the nnU-Net framework. A publicly available presurgical MRI dataset comprising 85 FCD subjects and 25 healthy controls was used to evaluate eight input configurations, including conventional MRI contrasts (T1w and FLAIR), ratio-derived representations, and their multimodal combinations. To isolate the effect of MRI representation, all experiments employed identical preprocessing, network architecture, optimization strategy, and five-fold cross-validation. Among the evaluated single-modality representations, FLAIR achieved the strongest overall performance, whereas ratio-derived representations alone were insufficient for reliable identification of subtle FCD. Incorporating ratio-derived representations with conventional T1w and FLAIR images consistently improved lesion delineation, with the four-channel multimodal configuration achieving the highest overall Dice score (0.376), representing a 5.0% relative improvement over the conventional T1w+FLAIR representation. These findings demonstrate that MRI representation design is an important yet underexplored component of deep learning-based FCD segmentation and should be optimized alongside network architecture.

Problem

• Focal Cortical Dysplasia
• MRI representation
• deep learning
• image segmentation
• epilepsy

Innovation

• MRI representation
• FCD segmentation
• ratio-derived MRI
• multimodal fusion
• nnU-Net benchmarking