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

Summary

This study investigates the impact of Glioma-pretrained federated learning on external generalization in multi-modal MRI meningioma segmentation

Abstract

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.

Introduction

Meningiomas are the most prevalent primary intracranial neoplasm in adults, accounting for approximately 38% of all primary brain and central nervous system tumors in the United States¹. Originating from meningothelial (arachnoid cap) cells, they encompass World Health Organization (WHO) grades I–III, spanning a wide clinical spectrum: grade-I tumors (approximately 80%) are generally associated with favorable outcomes following gross total resection, whereas grade-II (atypical) and grade-III (anaplastic) lesions exhibit substantially higher recurrence rates of 20–40% and exceeding 70% at ten years, respectively²

Protocol

This study was a retrospective clinical study. The study protocol was reviewed and approved by the Ethics Committee of Second People’s Hospital of Shenzhen for the collection and use of the SPHS clinical dataset, with the approval number 20190910. Written informed consent was obtained from all patients included in the SPHS cohort prior to enrollment in this study.

Patient data

This study used three datasets: BraTS2023-Men, BraTS2023-Gli, and an external clinical cohort from SPHS (SPHS-Men). Their characteristics are summarized below.

BraTS2023-Men

Results

The centralized UMamba 2D model (Strategy 1) achieved competitive in-domain performance on the BraTS2023-Men internal test set, with a case-averaged DSC of 0.8958, IoU of 0.8356, FN of 1,902, FP of 667, TN of 8,902,814, and TP of 22,616. When evaluated on the SPHS external validation cohort, the centralized model yielded a DSC of 0.7452 ± 0.1992 and an IoU of 0.6241 ± 0.1941, corresponding to a cross-center generalization gap of ΔDSC = 0.1506 (Table 1, ID 1). As an additional self-configuring baseline, n.......

Discussion

The central finding of this study is that initialization quality—rather than the federated aggregation protocol itself—is the decisive factor in closing the cross-center generalization gap for privacy-preserving meningioma segmentation. While the centralized UMamba 2D model achieved strong in-domain performance on the BraTS2023-Men cohort (DSC = 0.8958), it suffered a substantial degradation when evaluated on the independent SPHS clinical dataset (Δ DSC = 0.1506). This performance drop underscores the vu.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BraTS2023-Gli dataset	ASNR-MICCAI BraTS Challenge / Synapse (Sage Bionetworks)	N/A	Public glioma MRI dataset used for glioma-pretrained initialization. In this study, 450 cases were used under the binary tumor-versus-background formulation. https://www.synapse.org/Synapse:syn51156910/wiki/622351
BraTS2023-Men dataset	ASNR-MICCAI BraTS Challenge / Synapse (Sage Bionetworks)	N/A	Public meningioma MRI dataset used as the source cohort for model development and internal evaluation. In this study, 450 cases were used. https://arxiv.org/abs/2305.07642
DeepBet	Academic software by Fisch et al.	N/A	Brain extraction tool applied to the T1-weighted image to generate a brain mask for the SPHS cohort. https://arxiv.org/abs/2308.07003
MONAI	Project MONAI	Version 1.3	Medical imaging AI framework used for data loading and deep learning workflow implementation. https://docs.nvidia.com/monai/index.html
NVIDIA GeForce RTX 3090 GPU	NVIDIA	GeForce RTX 3090	Graphics processing unit with 24 GB GDDR6X memory used for model training and computation. https://www.nvidia.com/en-me/geforce/graphics-cards/30-series/rtx-3090-3090ti/
PyTorch	PyTorch Foundation, Linux Foundation	Version 2.0.1	Deep learning framework used to implement and train the UMamba 2D segmentation models. https://pytorch.org/foundation/
Python	Python Software Foundation	Version 3.10	Programming environment used for model training, preprocessing, evaluation, and statistical analysis. Replace with the exact Python version if known. https://www.python.org/psf-landing/
SPHS-Men clinical cohort	Second People’s Hospital of Shenzhen	N/A	External clinical multi-modal MRI cohort used for independent evaluation. A total of 174 cases were included.
UMamba 2D model implementation	Bowang-lab / U-Mamba authors	U-Mamba 2D configuration	State space model-based segmentation architecture used for binary meningioma segmentation. https://u-mamba.github.io/