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.
The main goals of this project are (1) to develop a computer aided detection (CAD) system to assist radiologists in detection of bladder and upper urinary tract abnormalities on multi-detector row CT urography (CTU) using advanced computer vision techniques and (2) to evaluate the effects of CAD on radiologists'detection of lesions on CTUs. The proposed CAD system for CTU will be a new and unique application of computerized techniques for analysis of urothelial neoplasms. The relevance of this project to public health is that CAD can potentially increase the efficacy of CTU for urothelial neoplasm detection by improving the performance and reducing the variability of both the experienced and the less experienced radiologists. Accurate identification of the cause of disease such as hematuria by CTU can spare the patient considerable effort of undergoing a potentially large number of imaging studies, and thus reduce cost by eliminating the additional imaging. Early detection can improve the prognosis and survival of the patients.
|Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping et al. (2014) CT urography: segmentation of urinary bladder using CLASS with local contour refinement. Phys Med Biol 59:2767-85|
|Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M et al. (2013) Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography. Acad Radiol 20:148-55|