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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 3D图像分析算法，用于自动计算肺肿瘤的地面玻璃透明度（GGO） PI：教授。 Chandra Kambhamettu 地面玻璃是指朦胧的不透明度的HRCT出现，并不掩盖 相关的肺部血管。这种外观是由实质异常引起的 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，图像像素的出现，其中中的像素被模糊 与其他“厚/不透明”结节像素相比，不透明度较小。 CT数据来自Christiana Care的Helen F. Graham癌症中心。 数据获取将不使用所有患者标识符，并根据 授予IRB批准的协议。 目标1：肺和结节的时间序列对齐（S）。目标是获得全球刚性， CT肺和肿瘤数据之间的全局非刚性，局部非凝聚的比对参数 在不同的时间实例中获得。我们将肺部和结节进行分割，使用扩展的superquadrics进行建模，然后在不同的时间实例数据之间进行基于样条的非固定运动估计。 AIM 2：GGO比率估计和肺结节的3D测量。 GGO像素是使用每个结节的建议训练系统识别的。然后进行测量，由直径，覆盖面积，体积和GGO覆盖范围组成，并可视化。 研究问题包括提出的图像分析和可视化工具的可行性，以帮助进行高分辨率计算机层析成像图像的放射学分析，以自动检测，提取，时间隔离肺结节，并用 地面玻璃不透明度（GGO）比率。结果指标包括对开发系统的定性，定量评估，以帮助选择治愈性有限切除的候选物。