Unified Adult–Pediatric Glioma Segmentation via Synergistic MAE Pretraining and Boundary-Aware Refinement

Accurate brain tumor segmentation in both adult and pediatric populations remains a challenge due to substantial differences in brain anatomy, tumor distribution, and subregion size. This study proposes a unified segmentation framework based on nnU-Net, integrating encoder-level self-supervised pretraining with a lightweight, boundary-aware decoder. The encoder is initialized using a large-scale 3D masked autoencoder pretrained on brain MRI, while the decoder is trained with a hybrid loss function that combines region-overlap and boundary-sensitive terms. A harmonized training and evaluation protocol is applied to both the BraTS-GLI (adult) and BraTS-PED (pediatric) cohorts, enabling fair cross-cohort comparison against baseline and advanced nnU-Net variants. The proposed method improves mean Dice scores from 0.76 to 0.90 for adults and from 0.64 to 0.78 for pediatric cases, while reducing HD95 from 4.42 to 2.24 mm and from 9.03 to 6.23 mm, respectively. These results demonstrate that combining encoder-level pretraining with decoder-side boundary supervision significantly enhances segmentation accuracy across age groups without adding inference-time computational overhead.