Improving out-of-domain generalization in Multiple Sclerosis detection and segmentation using Random Convolutions

Brain lesion segmentation is critical for diagnosing and monitoring neurological diseases such as Multiple Sclerosis (MS). However, lesion variability and differences in scanners and acquisition techniques pose a significant challenge to the robust generalization of automated segmentation models beyond their training domain. Traditional augmentations, such as rotation, intensity shifts, and scalings, often fail to capture the wide diversity observed across patient cases, limiting model generalizability. Random Convolutions (RC) address this limitation by introducing diverse intensity variations while preserving anatomical structures. Using an nnUNet-based model enhanced with RC augmentations, we achieved 5th place in the MSLesSeg challenge, highlighting that RC augmentations offer competitive in-domain performance. Building on this, we further assess model performance, both in terms of lesion detection and segmentation, in- and out-of-domain. We compare RC with several state-of-the-art augmentation and domain generalization strategies and show that an nnUNet trained with the RC augmentation is competitive in-domain and demonstrates superior generalization performance. • RC augmentation enhances out-of-domain performance in MS lesion detection and segmentation. • The model achieves a top-5 ranking in the MSLesSeg challenge with RC-enhanced nnUNet . • RC improves detection of small lesions, particularly under domain shift. • RC achieves optimal performance with mid-sized kernels ( k = 5–7) and moderate layer depths ( L = 6–8). • RC is a simple yet effective strategy for robust MS lesion segmentation.