The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.
The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.
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DOI：
10.1088/1361-6560/ac9449
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发表时间：
2022-10-12
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
分散式数据分析成为医疗保健领域越来越受欢迎的选择，因为它强调了在合作机构之间共享主要患者数据的需求。这突出表明，需要根据统一的标准进行一致的协调数据管理、预处理和确定感兴趣的区域。为此，本手稿描述了联合肿瘤分割（FeTS）工具的软件架构和功能。FeTS工具的主要目的是促进这种协调处理和生成脑磁共振成像上肿瘤亚室的金标准参考标签，并进一步实现跨分布在地球仪上的多个部位的肿瘤亚室描绘模型的联合训练，而无需共享患者数据。基于现有的开源工具，如Insight Toolkit（ITK）和Qt，FeTS工具旨在支持在集中式或联合式环境中训练针对肿瘤描绘的深度学习模型。FeTS工具的目标受众主要是对开发联合学习模型感兴趣的计算研究人员，并有兴趣加入全球联盟以实现这一目标。该工具在https://github.com/FETS-AI/Front-End上开源。
De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria. Towards this end, this manuscript describes the Federated Tumor Segmentation (FeTS) tool, in terms of software architecture and functionality. The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data. Building upon existing open-source tools such as the Insight Toolkit (ITK) and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced at https://github.com/FETS-AI/Front-End.
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影响因子：
19.7
作者：
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通讯作者：
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影响因子：
19.7
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