A Unified Brain Extraction Framework Powered by Structure-Intensity Disentanglement-Based Data Augmentation

Brain extraction is a fundamental step in neuroimaging analysis, yet existing methods are often constrained to specific modalities or age groups, limiting their applicability in largescale, lifespan studies. In this work, we present BrainExt, a unified foundation model for brain extraction across multiple imaging modalities and the whole lifespan. To address inter-modality and across-age variations, we first collect a large-scale multi-modal, lifespan brain dataset of 25,487 scans spanning from fetal to elderly subjects and introduce a Structure-Intensity Disentanglement Synthesis (SIDSyn) module to generate real-world distribution-aligned data for robust pre-training. The pre-trained model is subsequently fine-tuned on real clinical scans to better adapt to real-world data distributions. BrainExt demonstrates superior generalization and stability compared to existing methods, achieving high Dice and low surface distance across all modalities. Empowered by disentanglement-based augmentation and a two-stage training strategy, BrainExt provides a scalable, modality-agnostic foundation for unified brain extraction, establishing a strong basis for advancing neuroimaging research and clinical applications.