基于深度学习的局灶性皮质发育异常分割的MRI表征基准测试
Benchmarking MRI Representations for Deep Learning-Based Focal Cortical Dysplasia Segmentation
- The University of Queensland（昆士兰大学）
- I-MED Radiology（I-MED放射学）
- Bennett University（贝内特大学）
- Indian Institute of Technology Madras（印度马德拉斯理工学院）
- University College of Engineering, Osmania University（奥斯曼尼亚大学工程学院）
- Mater Hospital（玛特医院）
- Royal Brisbane and Women’s Hospital（皇家布里斯班和妇女医院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
研究针对局灶性皮质发育异常分割，利用nnU-Net框架对MRI表征进行基准测试，评估多种输入配置，发现FLAIR单模态性能最强，比率衍生表征单独不足，多模态配置可提高病变描绘，强调MRI表征设计在深度学习分割中的重要性。
中文摘要 AI 辅助
局灶性皮质发育异常（FCD）是耐药性局灶性癫痫的主要结构原因之一，其成像特征在传统磁共振成像（MRI）上难以准确识别和描绘。本研究使用nnU-Net框架对用于自动FCD分割的MRI表征进行系统基准测试。利用包含85名FCD受试者和25名健康对照的公开术前MRI数据集评估8种输入配置。结果表明，FLAIR在单模态表征中总体性能最强，比率衍生表征单独不足以可靠识别细微FCD，多模态配置能提高病变描绘，四通道多模态配置总体Dice分数最高。这些发现表明MRI表征设计是基于深度学习的FCD分割中重要但未充分探索的组成部分。
英文摘要
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.
