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.