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

用于癌症放射治疗自动图像分割的完全去中心化联合学习框架

• 批准号: 10303437
• 负责人: Yading Yuan
• 金额: $ 21.06万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10303437
• 关键词: Address; Adoption; Anatomy; Appearance; Area; Artificial Intelligence; Client; Clinical; Collaborations; Communities; Data; Data Aggregation; Data Pooling; Data Set; Data Sources; Databases; Decentralization; Development; Environment; Ethics; Goals; Health system; Healthcare Systems; Hospitals; Image; Image Analysis; Institution; Learning; Legal; Liver; Location; Machine Learning; Medical; Medical Imaging; Medical center; Medicine; Methods; Modeling; National Institute of Biomedical Imaging and Bioengineering; Organ; Ownership; Participant; Patients; Performance; Privacy; Process; Radiation Oncology; Radiation therapy; Research Priority; Resources; Risk; Scheme; Shapes; Software Tools; Source; Techniques; Time; Training; Validation; X-Ray Computed Tomography; automated segmentation; base; cancer radiation therapy; clinical practice; data sharing; deep learning; deep learning algorithm; flexibility; head and neck cancer patient; imaging Segmentation; improved; interoperability; liver imaging; metropolitan; novel; open source; patient privacy; peer; precision medicine; soft tissue; software infrastructure; success; task analysis; treatment planning; tumor

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 concerns, and is expected to have a catalytic impact on precision medicine by generalizing it to broader applications within medicine where a model needs to learn across multi-institutional data without sacrificing patient privacy.

虽然最近人工智能（AI）的激增在以下方面取得了显着进展： 各种图像分析任务，其在广泛的临床环境中的性能在很大程度上受到以下因素的限制 有限的泛化能力时，被应用到新的数据，主要是因为大多数模型已经 使用来自单个机构或具有有限训练数据的公共数据集的数据生成。汇总来自 不同的机构可以改善模型训练，但这种集中的数据共享实际上是具有挑战性的 由于各种技术、法律的、隐私和数据所有权障碍。该提案旨在解决这些障碍 通过开发一种新的流言联合学习（GFL）框架，通过学习 不同的数据源，无需共享患者数据。与传统的客户机/服务器相比， 联邦学习，如FedAvg，所提出的框架是完全分散的，因为模型在 本地数据集将以点对点的方式直接相互通信，使我们的方法更加强大 而且高效我们将在自动器官分割的任务中开发和评估所提出的方案 在接受放射治疗（RT）的肝癌和头颈癌（H&N）患者的CT图像中， 准确、可靠和有效地描绘那些处于危险中的器官（OAR）是临床上重要的，但在技术上， 具有挑战性的问题。我们假设用我们的框架训练的模型可以实现分割 性能不亚于从所有资源中汇集数据的模型。我们最近创建的动态 医疗保健系统模拟了一个多样化多机构环境，这使我们处于一个理想的环境中， 系统地评估我们的框架。具体目标包括：1）建立基于GFL的自动化OAR 细分框架，并开发支持软件基础设施; 2）优化基于GFL的自动 3）用400名肝癌患者和400名H&N癌症患者评估基于GFL的OAR分割框架 从一个大城市的卫生系统的四家医院收集的数据。该提案涉及两项关键研究 NIBIB的优先事项：基于机器学习的细分和方法，促进 在图像训练数据库中使用的注释。该项目的成功将大大增加 在不牺牲患者隐私的情况下，为模型训练提供多种数据，从而提高性能 以及分割模型在新数据上的推广。我们将开源这个框架， 实现更大规模的多机构合作，并加快人工智能驱动的自动化的临床采用。 更重要的是，该框架为主要的 将人工智能应用于医疗领域的障碍，在医疗领域，多机构数据共享的学习受到阻碍， 患者隐私问题，预计将对精准医疗产生催化作用， 更广泛的医学应用，其中模型需要在多机构数据中学习， 牺牲病人隐私