Improving Cross-Center Generalization for Multi-modal MRI Meningioma Segmentation via Glioma-Pretrained Federated...

Automated meningioma segmentation on multi-modal MRI remains challenging when models are transferred across institutions, because scanner protocols, image characteristics, and annotation styles may differ between centers. Federated learning (FL) provides a privacy-preserving strategy for multi-center model development, but standard aggregation may not fully overcome cross-center domain shift. This study aimed to quantify the external generalization gap in MRI meningioma segmentation and evaluate whether Glioma-pretrained FL could improve robustness without centralized data pooling. A UMamba 2D architecture was used for binary meningioma segmentation using T1, T1c, and T2 MRI as model inputs. The protocol included 450 BraTS2023-Men cases as the source-domain meningioma dataset and 174 independent clinical cases from our institute as the external validation cohort. Three final meningioma segmentation strategies were quantitatively evaluated under the same external validation setting: centralized training on BraTS2023-Men, meningioma-pretrained FL across three simulated clients, and Glioma-pretrained FL initialized from a BraTS2023-Gli source model before federated optimization. Centralized training on Glioma was used only to generate the glioma-pretrained initialization and was not reported as an independently evaluated final meningioma segmentation strategy. Model performance was assessed using the Dice similarity coefficient (DSC) and Intersection over Union (IoU). Centralized training on BraTS2023-Men showed a clear external generalization drop, with DSC decreasing from 0.8958 on the internal BraTS2023-Men test set to 0.7452 on the SPHS cohort. Meningioma-pretrained FL yielded lower external performance (DSC = 0.7122), whereas Glioma-pretrained FL achieved comparable performance to centralized training on BraTS2023-Men and improved over meningioma-pretrained FL (DSC = 0.7503; IoU = 0.6301; Holm-adjusted p < 0.001). These results suggest that glioma-pretrained initialization provides a more robust starting point for federated meningioma segmentation and may improve external generalization while preserving institutional data privacy.