Computer-aided Detection of Bladder Mass within Non-Contrast-enhanced Region of CT Urography (CTU)

摘要

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-intensity-projection-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 auto-initialized 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 non-contrast 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.