Computer-aided Detection of Pulmonary Embolism on CT Pulmonary Angiography

描述（由申请人提供）：肺栓塞（PE）是美国未经治疗的主要死亡原因之一。及时的诊断和治疗可以大大降低该病的死亡率和发病率。CT肺动脉造影（CTPA）已被报道是临床诊断PE的有效手段。PE的CT扫描的解释需要放射科医生进行大量的阅读，放射科医生必须在视觉上跟踪肺部的大量血管以检测可疑的PE。尽管作出了努力，但据报告，敏感性范围为53%至100%。计算机辅助诊断（CAD）是一种可行的方法，可以提高CTPA图像中PE检测的灵敏度和效率，并减少观察者之间的差异。拟议项目的总体目标是开发一个强大的CAD系统，该系统可以提供系统的PE筛查，并通过自动提醒放射科医生CTPA图像的2D切片和3D体积渲染显示上的可疑位置，作为第二意见。我们将开发先进的计算机视觉技术，以增强血管的特征，自动提取肺血管，重建血管树，检测候选PE，区分PE与正常肺结构，并识别真正的PE。该技术将专门设计用于分析CTPA图像上的复杂血管结构。该项目的具体目标包括：（1）收集大量数据集以开发和评估我们的CAD算法和系统，（2）建立性能评估的“金标准”，（3）开发鲁棒的肺血管分割方法，（4）开发鲁棒的肺血管树重建方法，以从血管树中准确跟踪肺血管，修剪静脉和周围广泛的肺部疾病，和标记重建的动脉树;（5）开发和改进PE检测算法，包括用于识别不同级别动脉分支处的可疑PE的多预筛选方法，用于开发分类方法的PE特征提取，基于特征分析的假阳性减少方法和基于模糊规则的、线性的或神经网络分类器，（6）建立肺栓塞指数的自动估计方法;（7）探索肺栓塞计算机检测的性能评价方法;（8）进行观察者ROC研究，评价CAD对放射科医师肺栓塞诊断准确性的影响。公共卫生相关性：本研究与公共卫生的相关性在于PE存在大量假阴性诊断。CAD将潜在地减少漏诊的PE并提高患者及时治疗的机会，从而降低死亡率并加速从这种疾病中恢复。

DESCRIPTION (provided by applicant): Pulmonary embolism (PE) is one of leading cause of death in the United States if untreated. Prompt diagnosis and treatment can dramatically reduce the mortality rate and morbidity of the disease. Computed tomographic pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of PE. Interpretation of a CT scan for PE demands extensive reading efforts from a radiologist who has to visually track a large number of vessels in the lungs to detect suspected PEs. Despite the efforts, the sensitivities were reported to range from 53% to 100%. Computer-aided diagnosis (CAD) can be a viable approach to improving the sensitivity and efficiency of PE detection in CTPA images, as well as reducing inter-observer variability. The overall goal of the proposed project is to develop a robust CAD system that can provide a systematic screening of PE and serve as a second opinion by automatically alerting the radiologists to suspicious locations on 2D slice and 3D volume rendering display of the CTPA images. We will develop advanced computer vision techniques to enhance the characteristics of vessels, automatically extract the pulmonary vessels, reconstruct the vessel tree, detect candidate PEs, differentiate PE from normal pulmonary structures, and identify the true PEs. The techniques will be specifically designed for analysis of the complex vascular structures on CTPA images. The specific aims of this project include (1) collecting a large data set to develop and evaluate our CAD algorithms and systems, (2) establishing "gold standard" for performance evaluation, (3) developing robust pulmonary vessel segmentation methods, (4) developing robust pulmonary vessel tree reconstruction method to accurately track pulmonary vessels, trim veins and surrounding extensive lung diseases from vessel tree, and label reconstructed arterial tree, (5) developing and improving PE detection algorithms, including multi-prescreening method for the identification of suspicious PEs at different levels of artery branches, PE features extraction for development of classification methods, false positive reduction method based on feature analysis and fuzzy rule-based, linear, or neural network classifiers, (6) developing automatic PE index estimation method, (7) exploring performance evaluation methodology for computerized detection of PEs, and (8) performing observer ROC study to evaluate the effects of CAD on radiologists' accuracy in PE diagnosis. PUBLIC HEALTH RELEVANCE: The relevance of this research to public health lies in the fact that there is substantial false-negative diagnosis of PEs. CAD will potentially reduce missed PEs and improve the chance of timely treatment of patients, thus reducing the mortality rate and speed up recovery from this condition.