COMPUTER AIDED DIAGNOSIS IN CT OF THE THORAX

胸部CT计算机辅助诊断

• 批准号: 6647008
• 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 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6647008
• 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.

拟议的研究的广泛，长期目标是 开发一个完全自动化的计算机系统，可以帮助放射科医生 螺旋结节中肺结核的检测和定量评估 胸腔的计算机断层扫描（CT）图像。这个系统可能会 改善肺癌患者的预后 诊断。人们广泛认识到螺旋CT是最敏感的 肺结节评估的成像方式。大量图像 然而，在CT扫描期间获得的数据使人可以检测到人类的结节检测 观察者是一项艰巨的任务。此外，区分结节和正常 诸如肺血管之类的解剖结构通常需要在 多个CT部分，每个部分包含必须评估的信息 通过放射科医生并吸收了体积数据的更大背景 在扫描过程中获得。该评估要求放射科医生在心理上 基于过度构建患者解剖结构的三维表示 在CT检查期间获得的50个部分图像。这项任务，虽然很麻烦 对于放射科医生，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 利用在CT检查期间获得的体积图像数据。灰色水平 基于阈值的技术将用于提取三维结构 来自CT图像数据。计算的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 区分与结节和结构相对应的结构 对应于正常解剖结构。这些定量信息也将允许 基于放射线外观的检测性能评估 结节。 拟议研究的具体目的是：（1）收集数据库 正常和异常的螺旋胸CT扫描，（2）发展自动化 在这些CT扫描中检测和定量评估肺结核的方法， （3）研究低剂量成像的结节外观差异 从肺癌筛查计划获得的螺旋胸CT扫描作为 反对标准的螺旋CT以及这些差异对 检测方案和（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的绩效的影响 识别肺结核。