We are developing a computerized system for detection of bladder cancer in CT urography. In this study, we used a deep-learning convolutional neural network (DL-CNN) to segment the bladder wall. This task is challenging due to differences in the wall between the contrast and non-contrast-filled regions, significant variations in appearance, size, and shape of the bladder among cases, overlap of the prostate with the bladder wall, and the wall being extremely thin compared to the overall size of the bladder. We trained a DL-CNN to estimate the likelihood that a given pixel would be inside the wall of the bladder using neighborhood information. A segmented bladder wall was then obtained using level sets with this likelihood map as a term in the level set energy formulation to obtain contours of the inner and outer bladder walls. The accuracy of the segmentation was evaluated by comparing the segmented wall outlines to hand outlines for a set of 79 training cases and 15 test cases using the average volume intersection % as the metric. For the training set, the inner wall achieved an average volume intersection of 90.0±8.7% and the outer wall achieved 93.7±3.9%. For the test set, the inner wall achieved an average volume intersection of 87.6±7.6% and the outer wall achieved 87.2±9.3%. The results show that the DL-CNN with level sets was effective in segmenting the inner and outer bladder walls.
Marshall Gordon, Lubomir Hadjiiski, Kenny Cha, Heang-Ping Chan, Ravi Samala, Richard H. Cohan, and Elaine M. Caoili, "Segmentation of inner and outer bladder wall using deep-learning convolutional neural network in CT urography," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013402 (Presented at SPIE Medical Imaging: February 13, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2255528.
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