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18 March 2014 Segmentation of urinary bladder in CT urography (CTU) using CLASS with enhanced contour conjoint procedure
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We are developing a computerized method for bladder segmentation in CT urography (CTU) for computeraided diagnosis of bladder cancer. A challenge for computerized bladder segmentation in CTU is that the bladder often contains regions filled with intravenous (IV) contrast and without contrast. Previously, we proposed a Conjoint Level set Analysis and Segmentation System (CLASS) consisting of four stages: preprocessing and initial segmentation, 3D and 2D level set segmentation, and post-processing. In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast (C) filled region separately and conjoins the contours with a Contour Conjoint Procedure (CCP). The CCP is not trivial. Inaccuracies in the NC and C contours may cause CCP to exclude portions of the bladder. To alleviate this problem, we implemented model-guided refinement to propagate the C contour if the level set propagation in the region stops prematurely due to substantial non-uniformity of the contrast. An enhanced CCP with regularized energies further propagates the conjoint contours to the correct bladder boundary. Segmentation performance was evaluated using 70 cases. For all cases, 3D hand segmented contours were obtained as reference standard, and computerized segmentation accuracy was evaluated in terms of average volume intersection %, average % volume error, and average minimum distance. With enhanced CCP, those values were 84.4±10.6%, 8.3±16.1%, 3.4±1.8 mm, respectively. With CLASS, those values were 74.6±13.1%, 19.6±18.6%, 4.4±2.2 mm, respectively. The enhanced CCP improved bladder segmentation significantly (p<0.001) for all three performance measures.
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Kenny Cha, Lubomir Hadjiiski, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, and Chuan Zhou "Segmentation of urinary bladder in CT urography (CTU) using CLASS with enhanced contour conjoint procedure", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352P (18 March 2014);

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