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Improving Out-of-Domain Generalization in Multiple Sclerosis Detection and Segmentation Using Random Convolutions

MCML Authors

Daniel Scholz

→ Group Benedikt Wiestler
AI for Image-Guided Diagnosis and Therapy

Daniel Rückert

Prof. Dr.

Director

Artificial Intelligence in Healthcare and Medicine

Benedikt Wiestler

Prof. Dr.

Principal Investigator

AI for Image-Guided Diagnosis and Therapy

Abstract

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.

article VSE+26