Joshi Shubham, Pant Millie, Malhotra Arnav, Deep Kusum, Snasel Vaclav
Department of Applied Mathematics and Scientific Computing, IIT Roorkee, Roorkee, India.
Mehta Family School of Data Science and Artificial Intelligence, IIT Roorkee, Roorkee, India.
Front Artif Intell. 2025 Jun 27;8:1601815. doi: 10.3389/frai.2025.1601815. eCollection 2025.
Focal cortical dysplasia (FCD) type II is a common cause of epilepsy and is challenging to detect due to its similarities with other brain conditions. Finding these lesions accurately is essential for successful surgery and seizure control. Manual detection is slow and challenging because the MRI features are subtle. Deep learning, especially convolutional neural networks, has shown great potential in automating image classification and segmentation by learning and extracting features. The nnU-Net framework is known for its ability to adapt its settings, including preprocessing, network design, training, and post-processing, to any new medical imaging task. This study employs an automated slice selection approach that ranks axial FLAIR slices by their peak voxel intensity and retains the five highest-ranked slices per scan, thereby focusing the network on lesion-rich slices and uses nnU-Net to automate the segmentation of FCD type II lesions on 3D FLAIR MRI images. The study was conducted on 85 FCD type II subjects and results are evaluated through 5-fold cross-validation. Using nnU-Net's flexible and robust design, this study aims to improve the accuracy and speed of lesion detection, helping with better presurgical evaluations and outcomes for epilepsy patients.
II型局灶性皮质发育不良（FCD）是癫痫的常见病因，由于其与其他脑部疾病相似，检测具有挑战性。准确发现这些病变对于成功手术和控制癫痫发作至关重要。手动检测缓慢且具有挑战性，因为MRI特征很细微。深度学习，尤其是卷积神经网络，通过学习和提取特征，在图像分类和分割自动化方面显示出巨大潜力。nnU-Net框架以其能够将包括预处理、网络设计、训练和后处理在内的设置适应任何新的医学成像任务而闻名。本研究采用一种自动切片选择方法，根据轴向液体衰减反转恢复（FLAIR）切片的体素峰值强度对其进行排序，并在每次扫描中保留排名最高的五张切片，从而使网络专注于富含病变的切片，并使用nnU-Net对三维FLAIR MRI图像上的II型FCD病变进行分割自动化。该研究对85名II型FCD受试者进行，结果通过五折交叉验证进行评估。利用nnU-Net灵活且强大的设计，本研究旨在提高病变检测的准确性和速度，有助于为癫痫患者进行更好地术前评估并改善预后。
