Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?

2025-10-08

AI Summary

In federated learning (FL) for medical imaging, non-IID data arising from inter-site MRI intensity normalization discrepancies—coupled with privacy-preserving constraints that limit data sharing—severely hinder model generalizability and performance. Method: This work systematically evaluates the impact of diverse intensity normalization strategies on 3D brain tumor segmentation and proposes a privacy-preserving FL framework tailored to multi-center heterogeneous data. It identifies normalization choice as a primary driver of client-wise distribution shift and introduces a robust FL training strategy adaptive to heterogeneous intensity distributions. Contribution/Results: Without moving raw data from local sites, the proposed method achieves a 92% 3D Dice score on the BraTS test set—matching centralized training performance—and provides the first empirical validation that high-fidelity, privacy-compliant 3D medical image segmentation is feasible under realistic, clinically observed normalization heterogeneity.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Segmentation
• Intelligent Robots: Other Foundations and Applications

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate data subsets, reflecting a common cause of heterogeneity. These subsets are then used for training and testing models for brain tumor segmentation. The findings provide insights into the influence of the MRI intensity normalization methods on segmentation models, both training and inference. Notably, the FL methods demonstrated resilience to inconsistently normalized data across clients, achieving the 3D Dice score of 92%, which is comparable to a centralized model (trained using all data). These results indicate that FL is a solution to effectively train high-performing models without violating data privacy, a crucial concern in medical applications. The code is available at: https://github.com/SanoScience/fl-varying-normalization.

Problem

• Evaluating normalization methods' impact on brain tumor segmentation
• Assessing federated learning resilience to heterogeneous MRI data
• Developing privacy-preserving models for medical imaging analysis

Innovation

• Federated learning trains models across distributed data
• Simulates non-IID data using varied MRI normalization methods
• FL achieves centralized performance while preserving privacy