Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)

Kenny H. Cha; Lubomir M. Hadjiiski; Heang-Ping Chan; Elaine M. Caoili; Richard H. Cohan; Alon Weizer; Chuan Zhou

doi:10.1117/12.2216289

24 March 2016 Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)

Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Alon Weizer, Chuan Zhou

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97853W (2016) https://doi.org/10.1117/12.2216289
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). We have previously developed methods for detection of bladder masses within the contrast-enhanced region of the bladder. In this study, we investigated methods for detection of bladder masses within the non-contrast enhanced region. The bladder was first segmented using a newly developed deep-learning convolutional neural network in combination with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensityprojection- based method. The non-contrast region was smoothed and a gray level threshold was employed to segment the bladder wall and potential masses. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify lesion candidates as a prescreening step. The lesion candidates were segmented using our autoinitialized cascaded level set (AI-CALS) segmentation method, and 27 morphological features were extracted for each candidate. Stepwise feature selection with simplex optimization and leave-one-case-out resampling were used for training and validation of a false positive (FP) classifier. In each leave-one-case-out cycle, features were selected from the training cases and a linear discriminant analysis (LDA) classifier was designed to merge the selected features into a single score for classification of the left-out test case. A data set of 33 cases with 42 biopsy-proven lesions in the noncontrast enhanced region was collected. During prescreening, the system obtained 83.3% sensitivity at an average of 2.4 FPs/case. After feature extraction and FP reduction by LDA, the system achieved 81.0% sensitivity at 2.0 FPs/case, and 73.8% sensitivity at 1.5 FPs/case.

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Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Alon Weizer, and Chuan Zhou "Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853W (24 March 2016); https://doi.org/10.1117/12.2216289

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