COMPUTER AIDED DIAGNOSIS IN CHEST RADIOGRAPHY

描述（改编自申请人摘要）：申请人提出， 开发用于检测的计算机辅助诊断（CAD）方案， 数字胸部图像中肺结节的表征。促进发展 可靠和可预测的CAD方案，他们建议建立一个大型的 数据库，其中有2 000例胸片，包括1 000个结节 例和1 000例非结节病例，与Richard M.斯隆医学博士， 华盛顿大学Mallinckrodt放射学研究所 安排一个先进的CAD方案检测肺结节将 通过结合三种减影技术--时间减影， 对侧减影和能量减影--为了实现， 平均而言，80-90%的高灵敏度， 每个胸部图像大约0.5。他们将调查 时间减影技术在提高微小结节检测中的应用 与肋骨重叠，也减少了假阳性的数量， 肋骨交叉，当同一患者的先前胸部图像可用时。 外侧减影，这是一种新的技术，消除周围 将检查单个PA胸部图像中的肋骨，以进行增强， 重叠结节的检测和假阳性数量的减少。 他们还将调查能量减影软组织的有用性 用于改进肺结节计算机化检测的成像 常规胸部图像。除了探测任务，他们还将 制定计算机辅助设计方案，用于描述结核的特征， 良性和恶性结节之间的区别此表征任务是提供 肺结节转移的可能性基于定量分析， 由计算机和/或由放射科医师检测的结节的图像特征。与 他们提出， 开发一个计算机辅助设计工作站原型，并进行试验研究， CAD方案在检测和表征 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.