COMPUTER AIDED DIAGNOSIS IN CT OF THE THORAX

胸部CT计算机辅助诊断

• 批准号: 6782667
• 负责人: Samuel G. Armato
• 金额: $ 23.34万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-07 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6782667
• 关键词: bioimaging /biomedical imaging; biomedical automation; computed axial tomography; computer assisted diagnosis; diagnosis design /evaluation; human data; lung neoplasms; neoplasm /cancer diagnosis

The broad, long term objective of the proposed research is to develop a fully automated, computerized system that will assist radiologists in the detection and quantitative assessment of pulmonary nodules in helical computed tomography (CT) images of the thorax. This system will potentially improve the prognosis of patients with lung cancer by contributing to earlier diagnosis. It is widely recognized that helical CT is the most sensitive imaging modality for the valuation of lung nodules. The large amount of image data acquired during a CT scan, however, makes nodule detection by human observers a difficult task. Moreover, distinguishing between nodules and normal anatomy such as pulmonary vessels typically requires visual comparison among multiple CT sections, each of which contains information that must be evaluated by a radiologist and assimilated into the larger context of the volumetric data acquired during the scan. This evaluation requires the radiologist to mentally construct a three-dimensional representation of patient anatomy based on over 50 section images acquired during a CT examination. This task, while cumbersome for radiologists, may be efficiently handled by a computerized method. The proposed research project will investigate the two-dimensional and three-dimensional structure of lung nodules in helical CT images to fully exploit the volumetric image data acquired during a CT examination. Gray-level threshold-based techniques will be used to extract three-dimensional structures from CT image data. Quantitative geometric and gray-level information computed for nodule candidates will be used as input to automated classifiers to distinguish between structures that correspond to nodules and structures that correspond to normal anatomy. This quantitative information will also allow for an evaluation of detection performance based on radiologic appearance of nodules. The specific aims of the proposed research are: (1) to collect databases of normal and abnormal helical thoracic CT scans, (2) to develop an automated method to detect and quantitatively assess pulmonary nodules in these CT scans, (3) to investigate differences in the appearance of nodules imaged in low-dose helical thoracic CT scans obtained from a lung cancer screening program as opposed to standard helical CT and the effect of these differences on the detection scheme, and (4) to evaluate the performance of the computerized detection scheme and its effect on the performance of radiologists in the task of identifying pulmonary nodules.

拟议研究的广泛、长期目标是 开发一个完全自动化的计算机化系统，帮助放射科医生 螺旋状肺结节的检测和定量评估 胸部计算机断层扫描 (CT) 图像。该系统将有可能 通过早期诊断来改善肺癌患者的预后 诊断。螺旋CT被广泛认为是最敏感的 用于评估肺结节的成像方式。图片量大 然而，CT 扫描期间获取的数据使得人类可以检测结节 观察员是一项艰巨的任务。此外，区分结节和正常 肺血管等解剖结构通常需要视觉比较 多个 CT 切片，每个切片都包含必须评估的信息 由放射科医生进行分析并融入更大的体积数据背景中 扫描期间获取的。该评估需要放射科医生在心理上 基于以上构建患者解剖结构的三维表示 CT 检查期间获取的 50 个切片图像。这个任务虽然繁琐 对于放射科医生来说，可以通过计算机方法有效地处理。这 拟议的研究项目将调查二维和 螺旋CT图像中肺结节的三维结构 利用 CT 检查期间获取的体积图像数据。灰度级 基于阈值的技术将用于提取三维结构 来自 CT 图像数据。计算定量几何和灰度信息 对于结节候选者将用作自动分类器的输入 区分对应于结节的结构和对应于结节的结构 符合正常解剖结构。这些定量信息也将允许 基于放射学外观的检测性能评估 结节。 本研究的具体目标是：（1）收集 正常和异常螺旋胸部 CT 扫描，(2) 开发自动化 在这些 CT 扫描中检测和定量评估肺部结节的方法， (3) 研究低剂量成像下结节外观的差异 从肺癌筛查项目中获得的螺旋胸部 CT 扫描如下 与标准螺旋 CT 不同，以及这些差异对 检测方案，以及（4）评估计算机化的性能 检测方案及其对放射科医生在任务中表现的影响 来识别肺部结节。

项目成果

Image annotation for conveying automated lung nodule detection results to radiologists.

用于向放射科医生传达自动肺结节检测结果的图像注释。

• DOI: 10.1016/s1076-6332(03)00116-8
• 发表时间: 2003
• 期刊: Academic radiology
• 影响因子: 4.8
• 作者: Armato3rd,SamuelG

Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index.

CT 扫描中肺结节的自动检测：利用径向梯度指数减少假阳性。

• DOI: 10.1118/1.2178450
• 发表时间: 2006
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Roy,ArunabhaS;Armato3rd,SamuelG;Wilson,Andrew;Drukker,Karen
• 通讯作者: Drukker,Karen