INTERACTIVE COMPUTER-AIDED DIAGNOSIS TOOLS FOR GROUND-GLASS OPACITY LUNG TUMORS

地面玻璃混浊肺肿瘤的交互式计算机辅助诊断工具

• 批准号: 8359615
• 负责人: CHANDRA KAMBHAMETTU
• 金额: $ 8.25万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8359615
• 关键词: Algorithms; Appearance; Area; Benign; Categories; Classification; Clinical; Computer software; Computer-Assisted Diagnosis; Delaware; Detection; Development; Dimensions; Effectiveness; Equation; Evolution; Funding; Glass; Grant; Graph; Growth; High Resolution Computed Tomography; Image; Image Analysis; Image Enhancement; Imaging technology; Lead; Lung; Lung Neoplasms; Lung nodule; Malignant - descriptor; Manuals; Maps; Measurement; Measures; Methods; Monitor; National Center for Research Resources; Nodule; Patients; Pattern; Principal Investigator; Property; Pulmonary vessels; Research; Research Infrastructure; Research Personnel; Resolution; Resources; Solid; Source; System; Techniques; Thoracic Surgeon; Three-Dimensional Image; Time; United States National Institutes of Health; Vision; Work; X-Ray Computed Tomography; base; bioimaging; clinical practice; computerized; cost; experience; improved; innovation; interest; novel; radiologist; time interval; tool; tumor; tumor growth

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. High-resolution Computed Tomography (HRCT) is frequently used to detect tumors in patients, and to monitor tumor growth or shrinkage at different time intervals during treatment. The accurate classification of a tumor into benign or malignant categories is critical to determine the appropriate treatment and CTs are often used to assess the effectiveness of a selected treatment. Advances in CT imaging technology have assisted in acquiring the images at increasingly high resolution; however, current algorithms are limited to measuring volume changes of the tumor rather than providing an accurate measurement of tumor growth in three dimensions. Of particular interest for this study are Ground-Glass Opacity (GGO) tumors that pose a special challenge to conventional image analysis algorithms, which are traditionally tuned toward detection of high gradient changes and thus would frequently miss GGO tumors. Ground-glass opacity refers to the appearance of a hazy opacity during high-resolution computed tomography (HRCT) that does not obscure the associated pulmonary vessels. This appearance results from parenchymal abnormalities that are below the spatial resolution of HRCT. In this study, we develop a novel three-dimensional (3D) method for interactive, automated and accurate segmentation and assessment of GGO tumors. The innovation of our method is the development of novel interactive 3D image analysis tool to extract GGO lung nodules, and perform analysis based on the resulting opacity map. To date, existing software algorithms are able to help detect and measure solid lung nodules based on available CT-image information; however, they are not capable of working on GGO tumors and estimating the overall GGO coverage of detected nodules in the lung. Current methods utilize manual expert analysis for this important task. We propose to measure quantitatively the opacity property of each pixel in a ground-glass opacity tumor from CT images. Our method results in an opacity map in which each pixel takes opacity value between 0-1. Given a CT image, we propose to accomplish the estimation by constructing a graph Laplacian matrix and solving a linear equations system, with assistance from some manually drawn scribbles for which the opacity values are easy to determine manually. The development of an automated GGO lung tumor detection will greatly improve the efficiency of routine radiological and oncological analysis. Our innovative approach for an objective assessment of GGO tumors will allow the radiologist or thoracic surgeon to evaluate the threedimensional evolution of the tumor and the dimensional changes detected by CT scans taken at different time spans, including changes in growth pattern, maximum areas/orientation of growth, and opacity changes. This proposed study is the first step toward the development of a computerized assessment of GGO tumors and, if successful, will lead to further translational efforts to integrate these techniques into clinical practice. The team brought together to successfully work on this effort is comprised of a thoracic surgeon, who acts as a clinical subject matter expert, and experienced researchers in image enhancement, automated vision and biomedical imaging.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 高分辨率计算机断层扫描 (HRCT) 经常用于检测患者的肿瘤，并在治疗期间的不同时间间隔监测肿瘤的生长或缩小。将肿瘤准确分类为良性或恶性类别对于确定适当的治疗方法至关重要，CT 通常用于评估所选治疗方法的有效性。 CT 成像技术的进步有助于获取越来越高分辨率的图像；然而，当前的算法仅限于测量肿瘤的体积变化，而不是在三个维度上提供肿瘤生长的准确测量。这项研究特别感兴趣的是毛玻璃不透明（GGO）肿瘤，它对传统图像分析算法提出了特殊的挑战，传统图像分析算法传统上是针对高梯度变化的检测而调整的，因此经常会错过 GGO 肿瘤。磨玻璃样混浊是指在高分辨率计算机断层扫描 (HRCT) 过程中出现的模糊混浊，不会遮挡图像 相关肺血管。这种外观是由于实质异常引起的 低于 HRCT 的空间分辨率。 在这项研究中，我们开发了一种新颖的三维 (3D) 方法，用于交互式、自动化和准确的 GGO 肿瘤分割和评估。我们方法的创新之处在于开发了新型交互式 3D 图像分析工具来提取 GGO 肺结节，并根据生成的不透明图进行分析。 迄今为止，现有的软件算法能够帮助检测和测量实性肺结节 基于可用的 CT 图像信息；然而，他们没有能力在GGO上工作 肿瘤并估计肺部检测到的结节的总体 GGO 覆盖范围。当前的方法利用专家手动分析来完成这项重要任务。我们建议定量测量 CT 图像中毛玻璃不透明肿瘤中每个像素的不透明度特性。我们的方法会生成一个不透明度贴图，其中每个像素的不透明度值在 0-1 之间。给定 CT 图像，我们建议通过构造图拉普拉斯矩阵并求解线性方程组来完成估计，并借助一些手动绘制的涂鸦（这些涂鸦的不透明度值很容易手动确定）。 自动化 GGO 肺部肿瘤检测的开发将大大提高常规放射学和肿瘤学分析的效率。我们对 GGO 肿瘤进行客观评估的创新方法将使放射科医生或胸外科医生能够评估肿瘤的三维演变以及在不同时间跨度进行的 CT 扫描检测到的尺寸变化，包括生长模式的变化、生长的最大面积/方向以及不透明度的变化。这项拟议的研究是开发 GGO 肿瘤计算机化评估的第一步，如果成功，将导致进一步的转化努力，将这些技术整合到临床实践中。成功完成这项工作的团队由一名担任临床主题专家的胸外科医生以及图像增强、自动视觉和生物医学成像领域经验丰富的研究人员组成。