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.

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