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

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $210,602

## 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 auto-
segmentation; 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 auto-
segmentation 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 concer...

## Key facts

- **NIH application ID:** 10303437
- **Project number:** 1R21EB030209-01A1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Yading Yuan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $210,602
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10303437

## Citation

> US National Institutes of Health, RePORTER application 10303437, A Fully Decentralized Federated Learning Framework for Automated Image Segmentation in Cancer Radiotherapy (1R21EB030209-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10303437. Licensed CC0.

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