Masked autoencoding, generalizable pretraining, and integrated experts for enhanced glioma segmentation.

Accurate glioma segmentation is critical for clinical diagnosis and treatment planning, yet remains challenging due to infiltrative tumor growth, heterogeneous imaging protocols, and scarcity of expert annotations. We present MAGPIE, a self-supervised learning framework that combines masked autoencoding, contrastive learning, and sparse mixture of experts to enable accurate glioma segmentation with minimal labeled data. By pretraining on 43,505 unlabeled multi-modal brain MRI scans, MAGPIE learns generalizable representations through a channel-agnostic architecture that handles varying modality configurations without protocol-specific preprocessing. The sparse MoE mechanism with top-2 routing allows specialized expert networks to emerge for different glioma subregions, while deformable attention mechanisms capture infiltrative margins and multi-scale features. Fine-tuning on only 20 labeled cases achieves 60.87% Dice score on BraTS21, a 2.59% absolute improvement over training from scratch, with 70.32% on out-of-distribution data demonstrating robust cross-domain generalization. These results reduce annotation requirements by 95% compared to typical supervised methods, directly addressing the data scarcity bottleneck in rare tumor subtypes and enabling deployment across heterogeneous clinical imaging systems.