Computer-aided Detection of Pulmonary Embolism on CT Pulmonary Angiography

CT肺血管造影计算机辅助检测肺栓塞

• 批准号: 8315984
• 负责人: CHUAN ZHOU
• 金额: $ 47.03万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8315984
• 关键词: Affect; Algorithms; Angiography; Archives; Arteries; Benign; Biological Neural Networks; Blood Vessels; Cause of Death; Characteristics; Classification; Complex; Computer Vision Systems; Computer-Assisted Diagnosis; Data; Data Set; Database Management Systems; Databases; Detection; Development; Diagnosis; Evaluation; Evaluation Methodology; Goals; Gold; Health; Image; Interobserver Variability; Label; Location; Lung; Lung diseases; Malignant - descriptor; Methods; Morbidity - disease rate; Patients; Performance; Public Health; Pulmonary Embolism; Pulmonary vessels; Reading; Recovery; Reporting; Research; Scanning; Scheme; Screening procedure; Second Opinions; Seeds; Slice; Speed; Structure; Structure of parenchyma of lung; System; Techniques; Testing; TimeLine; Training; Trees; United States; Veins; X-Ray Computed Tomography; base; clinical Diagnosis; computer aided detection; computerized; design; improved; indexing; mortality; radiologist; reconstruction; soft tissue

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.

描述（由申请人提供）：肺栓塞（PE）是美国的主要死亡原因之一，如果不及时治疗。及时诊断和治疗可显著降低该病的死亡率和发病率。计算机断层肺血管造影（CTPA）已被报道为PE临床诊断的有效手段。对CT扫描的PE进行解释需要放射科医生进行大量的阅读工作，放射科医生必须通过视觉跟踪肺部大量血管来检测可疑的PE。尽管做出了努力，但据报道，敏感性从53%到100%不等。计算机辅助诊断（CAD）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可变性。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PE的系统筛选，并通过自动提醒放射科医生在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。我们将发展先进的计算机视觉技术，增强血管特征，自动提取肺血管，重建血管树，检测候选PE，将PE与正常肺结构区分开来，并识别真正的PE。该技术将专门用于分析CTPA图像上复杂的血管结构。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)开发鲁棒肺血管分割方法；(4)开发鲁棒肺血管树重建方法，以准确跟踪肺血管，从血管树中修剪静脉和周围广泛的肺部疾病，并标记重建的动脉树。(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可疑PE的多重预筛选方法、用于开发分类方法的PE特征提取方法、基于特征分析和基于模糊规则、线性或神经网络分类器的假阳性降低方法；(6)开发PE自动指标估计方法；(7)探索PE计算机检测的性能评价方法；(8)进行观察ROC研究，评估CAD对放射科医师PE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有可能减少漏诊的pe，提高患者得到及时治疗的机会，从而降低死亡率，加快患者的康复。

项目成果

Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method.

• DOI: 10.1016/j.compmedimag.2011.04.001
• 发表时间: 2012-01
• 期刊: COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
• 影响因子: 5.7
• 作者: Zhou, Chuan;Chan, Heang-Ping;Chughtai, Aamer;Patel, Smita;Hadjiiski, Lubomir M.;Wei, Jun;Kazerooni, Ella A.
• 通讯作者: Kazerooni, Ella A.