Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography

MDCT 尿路造影计算机辅助检测尿路癌

• 批准号: 8665805
• 负责人: Lubomir M Hadjiyski
• 金额: $ 35.35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-02 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8665805
• 关键词: 3-Dimensional; Algorithms; Area; Benign; Bladder; Cancer Detection; Cancer Etiology; Cessation of life; Characteristics; Classification; Collection; Computer Vision Systems; Computer-Assisted Diagnosis; Computers; Database Management Systems; Databases; Detection; Development; Diagnosis; Disease; Early Diagnosis; Evaluation; Future; Goals; Hematuria; Image; Image Analysis; Investigation; Knowledge; Left; Lesion; Lower urinary tract; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Methods; Modeling; Morbidity - disease rate; Patients; Performance; Prevalence; Process; Public Health; Reading; Resources; Scanning; Slice; Staging; System; Techniques; Testing; Thick; Time; TimeLine; Training; United States; Ureter; Urinary tract; Urography; Urologic Cancer; Urologic Neoplasms; Urothelial Neoplasm; base; cancer type; computer aided detection; computerized; cost; design; detector; experience; graphical user interface; imaging modality; improved; innovation; malignant breast neoplasm; mortality; older patient; outcome forecast; public health relevance; radiologist; tool

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.

描述（由申请方提供）：尿路肿瘤是一种常见的癌症类型，可导致患者大量发病和死亡。 在美国，膀胱癌和上尿路癌每年导致14800人死亡。预计在2008年，会有71100宗膀胱癌及上尿路癌的新症被诊断出来。多排螺旋CT（MDCT）尿路造影是目前一种非常有前途的成像方式，用于早期发现膀胱和上尿路癌，这可能是血尿的原因。 血尿的患病率在老年患者中可高达19%。 MDCT尿路造影片（CTU）通常超过400层的解释是一项艰巨的任务，放射科医生必须在视觉上跟踪上尿路和下尿路，并寻找通常尺寸较小的病变。此外，一些膀胱病变可以在充满造影剂的膀胱区域中，而一些在没有造影剂的区域中。该项目的长期目标是开发一个有效的计算机辅助诊断（CADx）系统，以帮助放射科医生解释CTU。 在这个建议的项目中，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CTU上检测膀胱和上尿路癌的准确性。 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）容积CTU;（3）开发检测膀胱病变的算法;（4）开发检测上尿路病变的算法;（5）通过观察者ROC研究比较有无CAD患者CTU对膀胱和上尿路病变的检出准确性。为了完成这些任务，我们将开发新的图像分析技术，用于输尿管的自动跟踪和膀胱和输尿管的内壁和外壁的分割。新的方法将专门设计用于检测膀胱和输尿管中的病变候选者。我们将设计方法和3D测量来估计膀胱壁厚度的不对称性和检测输尿管壁增厚。 将开发特征提取技术和稳健的分类方法，用于使用提取的特征识别真阳性和消除假阳性病变。如果成功开发，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解释血尿患者的CTU方面的表现，从而可以在早期阶段检测到其他癌症。早期发现可改善患者的预后和生存。

项目成果

CT urography: segmentation of urinary bladder using CLASS with local contour refinement.

• DOI: 10.1088/0031-9155/59/11/2767
• 发表时间: 2014-06-07
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Cha K;Hadjiiski L;Chan HP;Caoili EM;Cohan RH;Zhou C
• 通讯作者: Zhou C

Detection of urinary bladder mass in CT urography with SPAN.

SPAN CT 尿路造影检测膀胱肿块。

• DOI: 10.1118/1.4922503
• 发表时间: 2015
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Cha,Kenny;Hadjiiski,Lubomir;Chan,Heang-Ping;Cohan,RichardH;Caoili,ElaineM;Zhou,Chuan
• 通讯作者: Zhou,Chuan

Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography.

• DOI: 10.1016/j.acra.2012.08.012
• 发表时间: 2013-02
• 期刊: ACADEMIC RADIOLOGY
• 影响因子: 4.8
• 作者: Hadjiiski, Lubomir;Chan, Heang-Ping;Caoili, Elaine M.;Cohan, Richard H.;Wei, Jun;Zhou, Chuan
• 通讯作者: Zhou, Chuan

Ureter tracking and segmentation in CT urography (CTU) using COMPASS.

使用 COMPASS 在 CT 尿路造影 (CTU) 中进行输尿管跟踪和分割。

• DOI: 10.1118/1.4901412
• 发表时间: 2014
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Hadjiiski,Lubomir;Zick,David;Chan,Heang-Ping;Cohan,RichardH;Caoili,ElaineM;Cha,Kenny;Zhou,Chuan;Wei,Jun
• 通讯作者: Wei,Jun