ITK-Lung: A Software Framework for Lung Image Processing and Analysis

ITK-Lung：肺部图像处理和分析的软件框架

• 批准号: 9325271
• 负责人: JAMES C GEE
• 金额: $ 60.57万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9325271
• 关键词: Address; Adopted; Advertisements; Algorithms; Ants; Atlases; Basic Science; Biological; Biological Markers; Clinical; Code; Communities; Complement; Computational algorithm; Computer Analysis; Computer software; Data; Data Set; Detection; Development; Diagnosis; Disease Progression; Documentation; Educational workshop; Employment; Environmental air flow; Evaluation; Goals; Human; Image; Image Analysis; Imagery; Imaging technology; Iowa; Lead; Legal patent; Libraries; Lobe; Lung; Lung diseases; Magnetic Resonance Imaging; Methodology; Methods; Modality; Modernization; Monitor; Multimodal Imaging; Nodule; Pathway interactions; Patients; Pennsylvania; Performance; Perfusion; Positron-Emission Tomography; Process; Publications; Publishing; Pulmonary Pathology; Reproduction; Research; Research Methodology; Research Personnel; Resources; S-nitro-N-acetylpenicillamine; Science; Scientist; Shapes; Site; Software Framework; Software Tools; Structure; Techniques; Training; Treatment outcome; United States National Institutes of Health; Universities; Virginia; Work; base; clinical practice; computing resources; experience; graphical user interface; image processing; image registration; imaging Segmentation; imaging biomarker; imaging potential; improved; indexing; innovation; insight; interoperability; lung development; lung imaging; lung lobe; neuroimaging; novel; open data; open source; prevent; programs; quantitative imaging; respiratory; software development; symposium; task analysis; technological innovation; tool; trend; user-friendly

Summary The development and proliferation of quantitative image analysis methods have accelerated research efforts and are having an increasingly significant impact in modern clinical practice. Although the research utility of these techniques has been amply demonstrated in determining longitudinal and groupwise trends, they are also becoming increasingly relevant in the clinical setting in providing biomarkers for aiding patient diagnoses, monitoring disease progression, and determining treatment outcomes. Increases in the capabilities and accessibility of computational facilities and a corresponding sophistication in computational algorithms have only made such practices more commonplace. One of the most significant hurdles for the pulmonary imaging field in adopting more quantitative clinical practices and exploring additional novel research pathways is the open availability of accurate, robust, and easy-to-use image analysis tools. This project addresses this important unmet need by bringing together leading expertise in pulmonary image analysis at the University of Pennsylvania, the University of Virginia, and the University of Iowa to develop, evaluate, and deploy a critical open-science resource under community support that would allow access to multi-modality data and tools for processing and analysis of lung imaging data. Toward this end, a comprehensive image analysis and data package, denoted as ITK-Lung, will be developed to address the specific needs of the multi-modality pulmonary imaging community. This first-of-its-kind resource will be enhanced by data- and domain-specific tuning that will accommodate a variety of user backgrounds and needs. These developments will be evaluated through their application to real-world use cases representing the state-of-the-science in lung imaging research. The successful completion of this project will help to fully realize the value of computational image analysis in pulmonary research and lead to new insight that may open novel ways to treat, cure and even prevent pulmonary disorders.

摘要 定量图像分析方法的发展和激增加速了研究 并在现代临床实践中产生越来越重要的影响。尽管 这些技术的研究效用在确定纵向和纵向 GroupWise趋势，它们在临床环境中也变得越来越相关， 用于辅助患者诊断、监测疾病进展和决定治疗的生物标志物 结果。提高计算设施的能力和可访问性，以及相应的 计算算法的复杂性只会让这种做法变得更加常见。 肺部影像领域采用更多量化临床的最大障碍之一 实践和探索其他新的研究途径是开放的、准确的、稳健的 和易于使用的图像分析工具。该项目解决了这一重要的未得到满足的需求 共同在宾夕法尼亚大学肺图像分析方面拥有领先的专业知识 弗吉尼亚大学和爱荷华大学共同开发、评估和部署一个关键的开放科学资源 在社区支持下，将允许访问多模式数据和工具以进行处理和 肺部影像资料分析。为此，一个全面的图像分析和数据包， 表示为ITK肺，将开发以满足多模式肺的特定需求 影像社区。这种首创的资源将通过特定于数据和领域的调优得到增强 这将适应不同的用户背景和需求。我们将对这些进展进行评估 通过将它们应用到代表肺部成像的最新科学状态的真实世界用例中 研究。该项目的顺利完成将有助于充分实现计算的价值 图像分析在肺部研究中的应用并带来新的见解，可能开辟新的治疗、治愈方法 甚至可以预防肺部疾病。