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.

项目摘要虽然最近的人工智能激增（AI）在 各种图像分析任务，它们在广泛的临床环境中的表现在很大程度上受到 应用于新数据时有限的概括能力，主要是因为大多数模型已经是 使用来自单个机构或公共数据集的数据生成有限的培训数据。汇总数据 不同的机构可以改善模型培训，但是这种集中的数据共享实际上具有挑战性 由于各种技术，法律，隐私和数据所有权障碍。该建议旨在解决这些障碍 通过开发一个新颖的八卦联合学习（GFL）框架，以通过从中学习来建立有效的AI模型 不同的数据源无需共享患者数据。与传统客户端/服务器相比 联合学习（例如FedAvg），提议的框架已完全分散，因为模型接受了培训 本地数据集将以点对点的方式直接互相通信，从而使我们的方法更强大 高效。我们将在自动器官细分任务中制定和评估提出的计划 在接受放射治疗（RT）治疗的肝脏和头颈（H＆N）癌症患者的CT图像中 那些处于危险的器官（OARS）的准确，健壮，有效的描述是临床上重要的，但在技术上 具有挑战性的问题。我们假设使用我们的框架训练的模型可以实现细分 性能不如从所有资源中汇总的数据的模型不如模型。我们最近创建的动态 医疗保健系统模仿了多样化的多机构环境，这使我们处于理想的环境中 系统地评估我们的框架。我们的具体目的包括：1）建立基于GFL的自动桨 分割框架，并开发支持软件基础架构； 2）优化基于GFL的自动 分割; 3）用400个肝脏和400 H＆N癌症患者评估基于GFL的OAR分割框架 从大都会卫生系统中的四家医院收集。该提案解决了两项关键研究 Nibib的优先级：基于机器学习的细分和促进互操作性的方法 图像培训数据库中使用的注释。该项目的成功将大大增加数字 以及用于模型培训的多种数据，而无需牺牲患者的隐私，从而提高了性能 并在新数据上分割模型的概括。我们将开源此框架，这可能 启用更大规模的多机构合作，可以加快临床采用AI驱动的自动 Rt中的分割。更重要的是，该框架为初级提供了灵活而强大的解决方案 将AI应用于医疗领域的障碍，在此，在多机构数据共享上学习受到阻碍 患者的隐私问题，预计将对精密医学产生催化影响，通过将其推广到 医学中更广泛的应用，其中模型需要在没有多机构数据的情况下学习 牺牲患者隐私。