TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis

2025-12-19

AI Summary

To address poor generalizability in multi-center brain tumor MRI segmentation caused by data silos and privacy constraints, this paper proposes an end-to-end federated segmentation framework integrating institutional-level personalized digital twins. We design a novel ViT-UNet hybrid backbone and extend FedAvg with local fine-tuning and digital twin modeling to enable adaptive, personalized optimization for heterogeneous, non-IID data. Evaluated on nine cross-institutional MRI datasets, our method achieves a Dice coefficient of 0.90 and sensitivity/specificity exceeding 90%, matching the performance of centralized training while strictly adhering to healthcare data governance requirements (i.e., “data never leaves the institution”). This work is the first to systematically embed the digital twin paradigm into federated medical image segmentation, significantly enhancing model robustness and clinical deployability.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Distributed Machine Learning & Federated Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance

Abstract

Brain tumor segmentation is critical in diagnosis and treatment planning for the disease. Yet, current deep learning methods rely on centralized data collection, which raises privacy concerns and limits generalization across diverse institutions. In this paper, we propose TwinSegNet, which is a privacy-preserving federated learning framework that integrates a hybrid ViT-UNet model with personalized digital twins for accurate and real-time brain tumor segmentation. Our architecture combines convolutional encoders with Vision Transformer bottlenecks to capture local and global context. Each institution fine-tunes the global model of private data to form its digital twin. Evaluated on nine heterogeneous MRI datasets, including BraTS 2019-2021 and custom tumor collections, TwinSegNet achieves high Dice scores (up to 0.90%) and sensitivity/specificity exceeding 90%, demonstrating robustness across non-independent and identically distributed (IID) client distributions. Comparative results against centralized models such as TumorVisNet highlight TwinSegNet's effectiveness in preserving privacy without sacrificing performance. Our approach enables scalable, personalized segmentation for multi-institutional clinical settings while adhering to strict data confidentiality requirements.

Problem

• Develops a privacy-preserving federated learning framework for brain tumor segmentation
• Addresses data privacy and generalization issues in multi-institutional medical imaging analysis
• Integrates digital twins and hybrid ViT-UNet models for accurate, real-time segmentation

Innovation

• Federated learning with digital twins for privacy
• Hybrid ViT-UNet model combining CNN and transformer
• Personalized fine-tuning on decentralized MRI datasets