CAD for CT Nodules in Lung Cancer Detection

描述(申请人提供)：拟议研究的目标是开发计算机辅助诊断(CAD)方案，用于检测和表征计算机断层扫描(CT)肺部图像中的肺结节。计算机输出将被用作“第二意见”，以协助放射科医生解释CT肺结节，以便及早发现肺癌。通过结合多模板匹配技术和大规模训练人工神经网络(MTANN)，将开发一种先进的CAD方案，以获得80%-90%的高灵敏度，以及每段低剂量螺旋CT(LDCT)图像约0.1或更低的低假阳性率。将开发一种新的减影CT技术，通过抑制包括肺血管在内的正常背景肺结构来增强微小肺结节。将研究减影CT技术与常规CT图像相结合的有效性，以提高肺结节计算机检测的整体性能。除了检测任务外，还将开发一种用于描述结节特征的计算机辅助设计方案，以区分良恶性病变。这项定性任务旨在根据对计算机和/或放射科医生检测到的结节的图像特征的定量分析，提供估计的恶性可能性。一种新的心理物理测量方法将基于人工神经网络，利用客观图像特征和对相似图像对的主观评分来确定，并将用于从大型数据库中选择一组与所讨论的未知结节相似的良、恶性结节，以辅助放射科医生的图像解释。随着我们期望达到的高水平的检测性能，将开发一个原型CAD工作站，并将进行观察者性能研究，以检验CAD方案在CT图像中的肺结节检测和分类方面的潜在用处。这些CAD计划将为放射科医生提供高度可疑的病变位置和/或良、恶性结节的定量测量，并有可能提高肺癌早期诊断的准确性，从而可能改善患者的预后。

DESCRIPTION (provided by applicant): The goal of the proposed research is to develop computer-aided diagnostic (CAD) schemes for detection and characterization of pulmonary nodules in computed tomography (CT) lung images. The computer output will be used as a "second opinion" to assist radiologists in their interpretation of CT lung nodules for early detection of lung cancer. An advanced CAD scheme will be developed by incorporating a multiple-template matching technique and also a massive training artificial neural network (MTANN) in order to achieve a high sensitivity of 80-90% with a low false positive rate of approximately 0.1 or less per section of low-dose helical CT (LDCT) images. A novel subtraction CT technique will be developed for enhancing subtle lung nodules by suppressing the normal background lung structure including pulmonary vessels. The usefulness of the subtraction CT technique in combination with the conventional CT image will be investigated in improving the overall performance in the computerized detection of lung nodules. In addition to the detection task, a CAD scheme for characterization of nodules will be developed in order to distinguish between benign and malignant lesions. This characterization task will be designed to provide the estimated likelihood of malignancy based on quantitative analysis of image features of nodules detected by computer and/or radiologists. A new psychophysical measure will be determined based on ANN by use of both objective image features and subjective ratings on pairs of similar images, and will be used to select a set of benign and malignant nodules from a large database, which would be similar to an unknown nodule inquestion, in order to assist radiologists' image interpretation. With the high level of detection performance that we expect to achieve, a prototype CAD workstation will be developed and observer performance studies will be carried out to examine the potential usefulness of CAD schemes on detection and classification of pulmonary nodules in CT images. These CAD schemes will provide the radiologists with the location of highly suspected lesions and/or quantitative measures for benign or malignant nodules, and have the potential to improve diagnostic accuracy in the early detection of lung cancer, which may lead to improved prognosis of patients.