# A Fully Decentralized Federated Learning Framework for Automated Image Segmentation in Cancer Radiotherapy

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $442,664

## Abstract

PROJECT SUMMARY While the recent surge of artificial intelligence (AI) has made remarkable progress in
various image analysis tasks, their performance in a broad range of clinical environment is largely restricted
by the limited generalization capability when being applied to new data, primarily because most models have
been generated using data from a single institution or public datasets with limited training data. Aggregating
data from different institutions could improve model training, but such centralized data sharing is practically
challenging due to various technical, legal, privacy and data ownership barriers. This proposal aims to address
these barriers by developing a novel gossip federated learning (GFL) framework to build an effective AI model
by learning from different data sources without the need of sharing patient data. As compared to the traditional
client/server federated learning such as FedAvg, the proposed framework is fully decentralized in that the
models trained in local datasets will directly communicate to each other in a peer-to-peer manner, making our
method more robust and efficient. We will develop and evaluate the proposed scheme in the task of automated
organ segmentation in CT images for liver and head and neck (H&N) cancer patients treated with radiation
therapy (RT) because accurate, robust and efficient delineation of those organs at risk (OARs) is a clinically
important but technically challenging problem. We hypothesize that the model trained with our framework can
achieve segmentation performance not inferior to a model with data pooled from all the resources. The
dynamics of our recently created healthcare system mimic a diverse multi-institutional environment, which
places us in an ideal setting to systematically evaluate our framework. Our specific aims include: 1) Establish
the GFL-based automated OAR segmentation framework, and develop the supporting software infrastructure;
2) Optimize the GFL-based autosegmentation; 3) Evaluate GFL-based OAR segmentation framework with
400 liver and 400 H&N cancer patients collected from four hospitals within a metropolitan health system. This
proposal addresses two key research priorities for NIBIB: machine learning based segmentation and
approaches that facilitate interoperability among annotations used in image training databases. The success
of this project will substantially increase the number and variety of data for model training without sacrificing
the patient privacy, and thus improve the performance and generalization of the segmentation model on new
data. We will open-source this framework, which may enable a larger scale of multi-institutional collaboration
and could expedite the clinical adoption of AI-driven autosegmentation in RT. More importantly, this framework
provides a flexible and robust solution to the primary barrier of applying AI to the medical domain where
learning on multi-institutional data sharing is impeded by patient privacy concerns, ...

## Key facts

- **NIH application ID:** 10831775
- **Project number:** 7R21EB030209-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Yading Yuan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $442,664
- **Award type:** 7
- **Project period:** 2023-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10831775

## Citation

> US National Institutes of Health, RePORTER application 10831775, A Fully Decentralized Federated Learning Framework for Automated Image Segmentation in Cancer Radiotherapy (7R21EB030209-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10831775. Licensed CC0.

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