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: 8167569
• 负责人: CHANDRA KAMBHAMETTU
• 金额: $ 7.56万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8167569
• 关键词: Algorithms; Appearance; Area; Benign; Categories; Classification; Clinical; Computer Retrieval of Information on Scientific Projects Database; Computer software; Computer-Assisted Diagnosis; Detection; Development; Dimensions; Effectiveness; Equation; Evolution; Funding; Glass; Grant; Graph; Growth; High Resolution Computed Tomography; Image; Image Analysis; Image Enhancement; Imaging technology; Institution; Lead; Lung; Lung Neoplasms; Lung nodule; Malignant - descriptor; Manuals; Maps; Measurement; Measures; Methods; Monitor; Nodule; Patients; Pattern; Property; Pulmonary vessels; Research; 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; 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. 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. 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。 高分辨率计算机断层扫描（HRCT）经常用于检测患者的肿瘤，并在治疗过程中以不同的时间间隔监测肿瘤的生长或收缩。将肿瘤分为良性或恶性类别对于确定适当的治疗至关重要，并且CTS通常用于评估所选治疗的有效性。 CT成像技术的进步有助于以越来越高的分辨率获取图像。但是，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准确测量肿瘤生长。这项研究特别有趣的是地面玻璃透明度（GGO）肿瘤，对传统图像分析算法构成了特殊挑战，这些算法传统上是针对检测高梯度变化的调整，因此经常会错过GGO肿瘤。地面玻璃不透明度是指高分辨率计算机断层扫描（HRCT）期间的朦胧不透明度的出现 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。 在这项研究中，我们开发了一种新型的三维（3D）方法，用于对GGO肿瘤的互动，自动化和准确的分割和评估。我们方法的创新是开发新型交互式3D图像分析工具，以提取GGO肺结节，并根据所得的不透明度图执行分析。 迄今为止，现有的软件算法能够帮助检测和测量固体肺结节 基于可用的CT图像信息；但是，他们没有能力从事GGO工作 肿瘤并估计肺中检测到的结节的总体GGO覆盖率。当前方法利用手动专家分析完成这项重要任务。我们建议从CT图像中定量测量每个像素的不透明度特性。我们的方法产生了一个不透明度图，在该图中，每个像素在0-1之间取得不透明度值。在给定CT图像的情况下，我们建议通过构造图形laplacian矩阵并求解线性方程系统来完成估计，并在一些手动绘制的涂鸦的帮助下，易于手动确定不透明度值。 自动GGO肺肿瘤检测的发展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化以及在不同时间跨度下进行的CT扫描检测到的尺寸变化，包括生长模式的变化，最大面积/方向的生长和透明度变化。这项拟议的研究是开发GGO肿瘤计算机评估的第一步，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。该团队聚集在一起以成功地进行这项工作，由胸外科医生组成，他是临床主题专家，以及经验丰富的图像增强，自动化视力和生物医学成像的研究人员。