3D IMAGE ANAL ALGORITHMS FOR AUTOMATIC COMP OF GROUND-GLASS OPACITY LUNG TUMOR

肺肿瘤磨玻璃影自动计算的 3D 图像分析算法

• 批准号: 7960176
• 负责人: CHANDRA KAMBHAMETTU
• 金额: $ 5.46万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7960176
• 关键词: Algorithms; Anus; Appearance; Area; Caliber; Cancer Center; Caring; Computer Retrieval of Information on Scientific Projects Database; Data; Delaware; Excision; Funding; Glass; Goals; Grant; Image; Image Analysis; Imagery; Institution; Lung Neoplasms; Lung nodule; Measurement; Modeling; Motion; Nodule; Outcome Measure; Patients; Protocols documentation; Pulmonary vessels; Quantitative Evaluations; Research; Research Ethics Committees; Research Personnel; Resolution; Resources; Series; Source; Surface; System; Thick; Three-Dimensional Image; Time; Training; United States National Institutes of Health; base; candidate selection; data acquisition; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 3D Image Analysis algorithms for Automatic computation of ground-glass opacity (GGO) of lung tumors PI:Prof. Chandra Kambhamettu Ground-glass refers to the HRCT appearance of a hazy opacity that does not obscure the associated pulmonary vessels. This appearance results from parenchymal abnormalities that are below the spatial resolution of HRCT. GGO area of a nodule can be observed in the HRCT images by the appearance of image pixels, where-in pixels are blurred and have less opacity compared to other "thick/opaque" nodule pixels. CT data is obtained from the Helen F. Graham Cancer Center of Christiana Care. Data acquisition will be used absent all patient identifiers and according to IRB approved protocol. Aim 1: Time-series alignment of lung and nodule(s). The goal is to obtain global rigid, global nonrigid, local nonrigid alignment parameters between the CT lung and tumor data obtained at different time instances. We segment the lung and nodules, use Extended-Superquadrics to model, and then perform spline based nonrigid motion estimation between different time instance data. Aim 2: GGO ratio estimation and 3D measurement of lung nodule. GGO pixels are identified using proposed training system for each nodule. Measurements are then performed consisting of the diameter, surface area of coverage, volume and GGO coverage, and visualized. Study questions include the feasibility of proposed image analysis and visualization tools for assistance in the radiologic analysis of high-resolution computed tomographic images in order to automatically detect, extract, time-align lung nodules, and classify them with ground-glass opacity (GGO) ratios. Outcome measures include the qualitative, quantitative evaluation of the developed system in assisting the selection of candidates for curative limited resection.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 自动计算肺肿瘤磨玻璃样阴影的三维图像分析算法 PI：Chandra Kambhamettu教授 毛玻璃样改变是指HRCT上模糊不清的阴影， 相关的肺血管这种表现是由于脑实质异常引起的 低于HRCT的空间分辨率。可以观察到结节的GGO区域 在HRCT图像中，由于图像像素的出现，其中像素被模糊 并且与其它“厚/不透明”结节像素相比具有较小的不透明性。 CT数据来自Helen F.克里斯蒂娜护理的格雷厄姆癌症中心。 将在没有所有患者标识符的情况下使用数据采集，并根据 IRB批准的方案。 目的1：肺和结节的时间序列对齐。我们的目标是获得全球刚性， CT肺部和肿瘤数据之间的全局非刚性、局部非刚性对准参数 在不同的时间点获得。我们分割肺和结节，使用扩展超二次曲面建模，然后执行基于样条的非刚性运动估计之间的不同时刻的数据。 目的2：肺结节的GGO比值估计及三维测量。GGO像素识别使用建议的训练系统为每个结节。然后进行测量，包括直径、覆盖表面积、体积和GGO覆盖，并可视化。 研究问题包括建议的图像分析和可视化工具的可行性，用于辅助高分辨率计算机断层扫描图像的放射学分析，以自动检测，提取，时间对齐肺结节，并将其分类。 毛玻璃样混浊（GGO）比率。结果的措施包括定性，定量评价的开发系统，以协助选择候选人的根治性有限切除术。