Bladder Wall Segmentation using U-Net based Deep Learning

摘要

We are developing a deep learning based U-Net (U-DL) model for bladder wall segmentation in CT urography (CTU) as a component of a computer-assisted pipeline for bladder cancer detection and treatment response assessment. This task is challenging due to variations in size and shape of the wall among cases, low contrast between the bladder wall and surrounding structures, and some walls being extremely thin and occasionally invisible compared to the overall size of the bladder. Our previous method used a deep-learning convolution neural network and level sets (DCNN-LS) within a user-input bounding box. In the current study, we propose two new methods for bladder wall segmentation: 1) the outer and inner bladder wall contour masks are generated to train two different U-DLs and the segmented bladder regions are subtracted to obtain the final bladder wall; 2) a combined wall mask for the bladder wall is generated by subtracting the hand-outlined bladder inner and outer contour masks, and a single U-DL is trained to segment the bladder wall. The new methods use only U-Net without level-set post-processing. Hand-segmented contours from 67 training and 14 validation cases were used for this study. The combined wall mask training method in particular shows promise in improving both accuracy and reducing pipeline complexity.