Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography

描述（申请人提供）：尿路肿瘤是一种常见的癌症类型，可导致患者大量发病率和死亡率。在美国，膀胱和上尿路癌每年导致14800人死亡。预计2008年将新增71100例膀胱及上尿路癌病例。多排CT （MDCT）尿路造影目前是一种非常有前途的早期检测膀胱癌和上尿路癌的成像方式，这可能是血尿的原因。在老年患者中，血尿的发生率可高达19%。对于放射科医生来说，解释通常超过400片的MDCT尿路图（CTU）是一项艰巨的任务，他们必须直观地跟踪上、下尿路并寻找通常较小的病变。此外，有些膀胱病变可发生在充满造影剂的膀胱区域，有些则发生在没有造影剂的膀胱区域。该项目的长期目标是开发一种有效的计算机辅助诊断（CADx）系统，以协助放射科医生解释ct。在这个拟议的项目中，我们将专注于开发第一个计算机辅助检测（CAD）系统，用于在CTU图像上检测膀胱和上尿路病变。我们假设使用CAD系统可以提高放射科医师在ct上发现膀胱和上尿路肿瘤的准确性。为了验证这一假设，我们将执行以下具体任务：(1)收集膀胱和上尿路恶性和良性病变的数据库；(2)开发新的计算机视觉技术来处理三维（3D）体积ctu；(3)开发膀胱病变检测算法；(4)开发检测上尿路病变的算法；(5)比较有无CAD的ct对膀胱和上尿路病变的检出率。为了完成这些任务，我们将开发新的图像分析技术，用于自动跟踪输尿管和分割膀胱和输尿管的内外壁。新的方法将专门用于检测膀胱和输尿管的候选病变。我们将设计方法和三维测量来估计膀胱壁厚度的不对称性和检测输尿管壁增厚。特征提取技术和稳健的分类方法将开发用于识别真阳性和消除假阳性病变使用提取的特征。如果开发成功，CAD系统可以潜在地提高放射科医生在检测尿路上皮肿瘤以及为血尿患者解释CTU方面的表现，从而在早期发现其他癌症。早期发现可以改善患者的预后和生存。

DESCRIPTION (provided by applicant): Urinary tract neoplasm is a common type of cancer that can cause substantial morbidity and mortality among patients. Bladder and upper urinary tract cancer causes 14800 deaths per year in the United States. It is expected that 71100 new bladder and upper urinary tract cancer cases would be diagnosed in 2008. Multi-detector row CT (MDCT) urography is currently a very promising imaging modality for early detection of bladder and upper urinary tract cancer, which can be a cause of hematuria. The prevalence of hematuria can be as high as 19% in elderly patients. Interpretation of MDCT urograms (CTU) that commonly exceeds 400 slices is a demanding task for radiologists who have to visually track the upper and lower urinary tract and look for lesions which usually are small in size. In addition, some bladder lesions can be in the bladder area filled with contrast and some in the area without contrast. The long term goal of the project is to develop an effective computer-aided diagnosis (CADx) system to assist radiologists in interpretation of CTUs. In this proposed project, we will concentrate on the development of the first computer-aided detection (CAD) system for the detection of bladder and upper urinary tract lesions on CTU images. We hypothesize that the use of CAD system can improve the radiologists' accuracy in detecting bladder and upper urinary tract cancer on CTUs. To test this hypothesis, we will perform the following specific tasks: (1) collect a database of bladder and upper urinary tract malignant and benign lesions; (2) develop new computer vision techniques to process 3-dimensional (3D) volumetric CTUs; (3) develop algorithms to detect bladder lesions; (4) develop algorithms to detect upper urinary tract lesions; and (5) compare the detection accuracy of bladder and upper urinary tract lesions on CTUs with and without CAD by observer ROC studies. In order to accomplish these tasks, we will develop new image analysis techniques for automated tracking of the ureter and segmentation of the inner and outer walls of the bladder and the ureter. New methods will be designed specifically for detection of lesion candidates in the bladder and the ureter. We will design methods and 3D measures for estimating asymmetries of the bladder wall thickness and detection of ureteral wall thickening. Feature extraction techniques and robust classification methods will be developed for identification of true positive and elimination of false positive lesions using the extracted features. If successfully developed, the CAD system can potentially improve the performance of the radiologists in detecting urothelial neoplasm as well as in interpreting CTU for patients with hematuria, allowing the detection of additional cancers at earlier stage. Early detection can improve the prognosis and survival of the patients.