We are developing a CAD system for detection of bladder cancer in CTU. In this study we investigated the application of deep-learning convolutional neural network (DL-CNN) to the segmentation of the bladder, which is a challenging problem because of the strong boundary between the non-contrast and contrast-filled regions in the bladder. We trained a DL-CNN to estimate the likelihood of a pixel being inside the bladder using neighborhood information. The segmented bladder was obtained from thresholding and hole-filling of the likelihood map. We compared the segmentation performance of the DL-CNN alone and with additional cascaded 3D and 2D level sets to refine the segmentation using 3D hand-segmented contours as reference standard. The segmentation accuracy was evaluated by five performance measures: average volume intersection %, average % volume error, average absolute % error, average minimum distance, and average Jaccard index for a data set of 81 training and 92 test cases. For the training set, DLCNN with level sets achieved performance measures of 87.2±6.1%, 6.0±9.1%, 8.7±6.1%, 3.0±1.2 mm, and 81.9±7.6%, respectively, while the DL-CNN alone obtained the values of 73.6±8.5%, 23.0±8.5%, 23.0±8.5%, 5.1±1.5 mm, and 71.5±9.2%, respectively. For the test set, the DL-CNN with level sets achieved performance measures of 81.9±12.1%, 10.2±16.2%, 14.0±13.0%, 3.6±2.0 mm, and 76.2±11.8%, respectively, while DL-CNN alone obtained 68.7±12.0%, 27.2±13.7%, 27.4±13.6%, 5.7±2.2 mm, and 66.2±11.8%, respectively. DL-CNN alone is effective in segmenting bladders but may not follow the details of the bladder wall. The combination of DL-CNN with level sets provides highly accurate bladder segmentation.