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

总结 定量图像分析方法的发展和扩散加速了研究 努力，并在现代临床实践中产生越来越重要的影响。虽然 这些技术的研究效用已经在确定纵向和纵向方向上得到了充分的证明。 随着分组趋势的发展，它们在临床环境中也变得越来越重要， 用于辅助患者诊断、监测疾病进展和确定治疗的生物标志物 结果。提高计算设施的能力和可及性， 计算算法的复杂性只会使这种实践变得更加普遍。 肺成像领域在采用更定量的临床诊断方面最重要的障碍之一是， 实践和探索其他新的研究途径是开放的准确，强大， 和易于使用的图像分析工具。该项目通过以下方式解决这一重要的未满足需求： 在宾夕法尼亚大学的肺部图像分析方面， 弗吉尼亚大学和爱荷华州大学合作开发、评估和部署一个关键的开放科学资源 在社区支持下，可以获得多模式数据和处理工具， 肺成像数据的分析。为此，一个全面的图像分析和数据包， 表示为ITK-Lung，将开发以满足多模态肺的特定需求 影像社区这是第一个此类资源，将通过特定于数据和域的调优来增强 这将适应各种用户背景和需求。将对这些发展进行评估 通过将其应用于代表肺部成像科学现状的真实用例， research.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应用，并带来新的见解， 甚至预防肺部疾病。

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.