Abstract

Focal Cortical Dysplasia (FCD) is a common disorder causing drug- resistant seizures and it is usually managed surgically. The precise segmentation of lesions from MRI and other neuroimaging data is important to diagnose and plan surgery accurately. Typical U-Net models cannot capture the significant textures, irregular shapes, and delicate boundaries of FCD lesions. To alleviate these limitations, we introduce an advanced deep-learning model called ImReN2UNET that addresses FCD universal lesion segmentation using a GLCM-based loss function and improved residual block architecture. The introduction of GLCM-based loss function sharpens model localization and delineation by emphasizing textural features and spatial interdependence in lesions. This is a significant improvement in this field because identifying minute and complex lesions is of the utmost importance in medical imaging. The ImReN2UNET architecture runs on the robust presence of the nn-U-Net architecture and combines residual learning with texture information to segment the FCD lesion regions. The proposed method yields considerably more precise and reliable lesion segmentation by experimental comparisons against state-of-the-art segmentation techniques. This technique provides a powerful instrument for the diagnosis and assessment of FCD and thus informs clinical decisions for better outcomes for the patients.