Federated Multi-organ Segmentation with Inconsistent Labels

TL;DR

This paper introduces Fed-MENU, a federated learning approach for multi-organ segmentation that effectively utilizes partially labeled data from multiple sources, overcoming label inconsistency issues in medical imaging.

Contribution

The paper proposes a novel federated multi-encoding U-Net (Fed-MENU) with organ-specific sub-networks and an auxiliary generic decoder to handle inconsistent labels across clients.

Findings

Fed-MENU outperforms existing models on six public datasets.

The method effectively leverages partially labeled data in federated learning.

Federated multi-organ segmentation improves clinical applicability.

Abstract

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.