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Proceedings Paper

Segmentation of inner and outer bladder wall using deep-learning convolutional neural network in CT urography
Author(s): Marshall Gordon; Lubomir Hadjiiski; Kenny Cha; Heang-Ping Chan; Ravi Samala; Richard H. Cohan; Elaine M. Caoili
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Paper Abstract

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.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013402 (3 March 2017); doi: 10.1117/12.2255528
Show Author Affiliations
Marshall Gordon, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Kenny Cha, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Ravi Samala, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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