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Proceedings Paper

Segmentation of urinary bladder in CT Urography (CTU) using CLASS
Author(s): Lubomir Hadjiiski; Heang-Ping Chan; Yuen Law; Richard H. Cohan; Elaine M. Caoili; Hyun-Chong Cho; Chuan Zhou; Jun Wei
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Paper Abstract

We are developing a computerized system for bladder segmentation on CTU, as a critical component for computer aided diagnosis of bladder cancer. A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with IV contrast (C). We are developing a Conjoint Level set Analysis and Segmentation System (CLASS) specifically for this application. CLASS performs a series of image processing tasks: preprocessing, initial segmentation, and 3D and 2D level set segmentation and post-processing, designed according to the characteristics of the bladder in CTU. The NC and the C regions of the bladder were segmented separately in CLASS. The final contour is obtained in the post-processing stage by the union of the NC and C contours. Seventy bladders (31 containing lesions, 24 containing wall thickening, and 15 normal) were segmented. The performance of CLASS was assessed by rating the quality of the contours on a 5-point scale (1= "very poor", 3= "fair", 5 = "excellent"). For the 53 partially contrast-filled bladders, the average quality ratings for the 53 NC and 53 C regions were 4.0±0.7 and 4.0±1.0, respectively. 46 NC and 41 C regions were given quality ratings of 4 or above. Only 2 NC and 5 C regions had ratings under 3. The average quality ratings for the remaining 12 completely no contrast (NC) and 5 completely contrast-filled (C) bladder contours were 3.3±1.0 and 3.4±0.5, respectively. After combining the NC and C contours for each of the 70 bladders, 46 had quality ratings of 4 or above. Only 4 had ratings under 3. The average quality rating was 3.8±0.7. The results demonstrate the potential of CLASS for automated segmentation of the bladder.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150J (23 February 2012); doi: 10.1117/12.912847
Show Author Affiliations
Lubomir Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Yuen Law, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Hyun-Chong Cho, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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