Federated Multi-organ Segmentation with Inconsistent Labels

Xuanang Xu, Hannah H. Deng, Jamie Gateno, Pingkun Yan

Research output: Contribution to journalArticlepeer-review


Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label consistency across clients by the existing methods largely narrows its application scope. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. Incorporating such partially labeled data into a unified federation is an unexplored problem with clinical significance and urgency. This work tackles the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net) is proposed to extract organ-specific features through different encoding sub-networks. Each sub-network can be seen as an expert of a specific organ and trained for that client. Moreover, to encourage the organ-specific features extracted by different sub-networks to be informative and distinctive, we regularize the training of the MENU-Net by designing an auxiliary generic decoder (AGD). Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods. Source code is publicly available at https://github.com/DIAL-RPI/Fed-MENU.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Medical Imaging
Early online dateApr 25 2023
StateE-pub ahead of print - Apr 25 2023


  • Data models
  • Deep learning
  • Feature extraction
  • Federated learning
  • Image analysis
  • Image segmentation
  • Inconsistent labels
  • Medical image segmentation
  • Task analysis
  • Training

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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