Benchmarking MRI Representations for Deep Learning-Based Focal Cortical Dysplasia Segmentation
Quick Answer
This study benchmarks MRI representations for deep learning-based segmentation of focal cortical dysplasia (FCD) using the nnU-Net framework.
Quick Take
It finds that FLAIR images outperform T1-weighted images in performance, and combining both with ratio-derived representations enhances lesion delineation, achieving a Dice score of 0.376, a 5% improvement over conventional methods.
Key Points
- Evaluated eight MRI input configurations for FCD segmentation.
- FLAIR images showed the strongest performance among single-modality representations.
- Ratio-derived representations alone were insufficient for reliable FCD identification.
- Four-channel multimodal configuration achieved the highest Dice score of 0.376.
- Study emphasizes the need to optimize MRI representation alongside network architecture.
Paper Resources
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~2 min readAbstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.15605 [cs.CV] |
| (or arXiv:2607.15605v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15605 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Soumen Ghosh Dr [view email]
[v1]
Fri, 17 Jul 2026 03:56:59 UTC (3,500 KB)
— Originally published at arxiv.org
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