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

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来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 高分辨率计算机断层扫描（HRCT）经常用于检测患者体内的肿瘤，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至关重要，CT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;然而，目前的算法仅限于测量肿瘤的体积变化，而不是提供三维肿瘤生长的准确测量。这项研究特别感兴趣的是磨玻璃混浊（GGO）肿瘤，这对传统的图像分析算法提出了特殊的挑战，传统的图像分析算法通常针对高梯度变化的检测进行调整，因此经常会错过GGO肿瘤。毛玻璃样阴影是指在高分辨率计算机断层扫描（HRCT）中出现模糊不清的阴影， 相关的肺血管这种表现是由于脑实质异常， 低于HRCT的空间分辨率。 在这项研究中，我们开发了一种新的三维（3D）方法，用于GGO肿瘤的交互式，自动化和准确的分割和评估。我们的方法的创新之处在于开发了一种新型的交互式3D图像分析工具来提取GGO肺结节，并根据所得的不透明度图进行分析。 到目前为止，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，它们不能在GGO上工作 肿瘤和估计肺中检测到的结节的总体GGO覆盖。目前的方法利用人工专家分析这一重要任务。我们建议定量测量的不透明性的每个像素在一个毛玻璃状阴影肿瘤的CT图像。我们的方法导致在不透明的地图，其中每个像素采取不透明度值之间的0-1。给定一个CT图像，我们建议完成的估计，通过构建一个图形拉普拉斯矩阵和求解一个线性方程组，从一些手工绘制的潦草的不透明度值很容易手动确定的援助。 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变以及在不同时间跨度进行CT扫描检测到的尺寸变化，包括生长模式的变化，最大面积/生长方向和不透明度变化。这项拟议的研究是发展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胸外科医生组成，他是一名临床主题专家，以及在图像增强，自动视觉和生物医学成像方面经验丰富的研究人员。

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.