COMPUTER AIDED DIAGNOSIS IN CHEST RADIOGRAPHY

描述(改编自申请人摘要)：申请人建议 开发计算机辅助诊断(CAD)方案，用于检测和 数字胸片中肺结节的特征。为了发展 可靠和可预测的CAD方案，他们建议建立一个大型 数据库中有2,000例胸片，其中包括1,000个结节 1 000例非结核病例，与理查德·M·斯隆医学博士合作， 华盛顿大学Mallinckrodt放射研究所，由一个财团 安排好了。一个先进的检测肺结节的计算机辅助设计方案将是 通过融合三种减法技术开发出来--时间减法， 对侧减法和能量减法--为了实现， 平均而言，灵敏度高达80%-90%，假阳性率低 每幅胸部图像大约0.5个。他们将调查这些项目的用处 时间减影技术在提高微小结节检出中的应用 与肋骨重叠，还减少了由于以下原因而导致的误报数量 肋骨交叉，当同一患者以前的胸部图像可用时。 对侧减影--一种去除周围神经的新技术 单个PA胸部图像中的肋骨将在 检测重叠的结节，减少假阳性的数量。 他们还将调查能量减影软组织的用处 用于改进的计算机检测肺结节的图像结合 常规的胸部影像。除了检测任务外，他们还将 开发用于描述结核特征的计算机辅助设计方案，以便区分 良性结节和恶性结节之间的区别。该表征任务是为了提供 肺结节癌变可能性的定量分析 由计算机和/或放射科医生检测到的结节的图像特征。与 他们提出，他们希望实现的高水平检测性能 开发原型CAD工作站并进行中试研究以检验 CAD方案在诊断和定性诊断中的临床应用价值 肺结节。

DESCRIPTION (Adapted from Applicant's Abstract): The applicants proposed to develop computer-aided diagnostic (CAD) schemes for detection and characterization of pulmonary nodules in digital chest images. For development of reliable and predictable CAD schemes, they proposed to establish a large database with 2,000 cases of chest radiographs, which include 1,000 nodule cases and 1,000 non-nodule cases, in collaboration with Richard M. Slone, M.D., Mallinckrodt Institute of Radiology, Washington University, under a consortium arrangement. An advanced CAD scheme for detection of lung nodules will be developed by incorporating three subtraction techniques--temporal subtraction, contralateral subtraction and energy subtraction--in order to achieve, on average, a high sensitivity of 80-90% with a low false positive rate of approximately 0.5 per chest image. They would investigate the usefulness of the temporal subtraction technique in increasing the detection of subtle nodules overlapped with ribs and also decreasing the number of false positives due to rib-crossings, when a previous chest image of the same patient is available. Contralateral subtraction, which is a novel technique for removal of peripheral ribs in a single PA chest image, will be examined for enhancement in the detection of overlapped nodules and reduction in the number of false positives. They would also investigate the usefulness of energy subtraction soft-tissue image for improved computerized detection of lung nodules in combination with conventional chest images. In addition to the detection task, they would to develop CAD schemes for characterization of nodules in order to distinguish between benign and malignant nodules. This characterization task is to provide the likelihood of malignance of lung nodules based on quantitative analysis of image features of nodules detected by computer and/or by radiologists. With the high level of detection performance that they expect to achieve, they propose to develop a prototype CAD workstation and carry out a pilot study to examine the clinical usefulness of CAD schemes on detection and characterization of pulmonary nodules.