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Related Concept Videos

  1. Home
  3. Federated Nnu-net For Privacy-preserving Medical Image Segmentation.
  1. Home
  3. Federated Nnu-net For Privacy-preserving Medical Image Segmentation.

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Federated nnU-Net for privacy-preserving medical image segmentation.

Grzegorz Skorupko1, Fotios Avgoustidis2, Carlos Martín-Isla2

  • 1Artificial Intelligence in Medicine Laboratory (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, 08007, Barcelona, Spain. grzegorz.skorupko@ub.edu.

Scientific Reports
|November 3, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

FednnU-Net enables decentralized medical image segmentation using federated learning, enhancing privacy. This framework achieves high performance across various segmentation tasks, democratizing AI in healthcare.

Keywords:
Data privacyFederated learningImage segmentationnnU-NetnnUNet

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • The nnU-Net framework is a leading tool for medical image segmentation but typically requires centralized data, posing privacy risks.
  • Centralized training can lead to sensitive patient data leakage and privacy violations.
  • Federated learning offers a decentralized approach to train models collaboratively while preserving patient privacy.

Purpose of the Study:

  • To introduce FednnU-Net, a federated learning extension for the nnU-Net framework.
  • To enable decentralized training of nnU-Net for medical image segmentation.
  • To enhance patient privacy in medical AI development.

Main Methods:

  • Development of FednnU-Net, a plug-and-play federated learning extension for nnU-Net.
  • Introduction of two novel federated methodologies: Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg).
  • Multi-modal segmentation experiments on breast, cardiac, and fetal datasets from 18 institutions.

    • Main Results:

    • FednnU-Net demonstrated high and consistent performance in breast, cardiac, and fetal segmentation tasks.
    • The proposed federated methodologies (FFE and AsymFedAvg) effectively enabled decentralized nnU-Net training.
    • Experiments validated the framework's robustness across diverse datasets and institutions.

    Conclusions:

    • FednnU-Net successfully extends nnU-Net capabilities to decentralized, privacy-preserving medical image segmentation.
    • The framework democratizes advanced AI research and clinical deployment by enabling collaborative, decentralized training.
    • Public release of the FednnU-Net framework aims to foster wider adoption and development in clinical settings.