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Proceedings Paper

Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets
Author(s): Kenny H. Cha; Lubomir M. Hadjiiski; Ravi K. Samala; Heang-Ping Chan; Richard H. Cohan M.D.; Elaine M. Caoili
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Paper Abstract

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.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978512 (24 March 2016); doi: 10.1117/12.2216852
Show Author Affiliations
Kenny H. Cha, Univ. of Michigan Health System (United States)
Lubomir M. Hadjiiski, Univ. of Michigan Health System (United States)
Ravi K. Samala, Univ. of Michigan Health System (United States)
Heang-Ping Chan, Univ. of Michigan Health System (United States)
Richard H. Cohan M.D., Univ. of Michigan Health System (United States)
Elaine M. Caoili, Univ. of Michigan Health System (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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