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20 March 2015 Computer-aided detection of bladder mass within contrast-enhanced region of CTU
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We are developing a computer-aided detection system for bladder cancer on CTU. The bladder was automatically segmented with our Conjoint Level set Analysis and Segmentation System (CLASS). In this preliminary study, we developed a system for detecting mass within the contrast-enhanced (C) region of the bladder. The C region was delineated from the segmented bladders using a method based on maximum intensity projection. The bladder wall of the C region was extracted using thresholding to remove the contrast material. The wall on each slice was transformed into a wall profile. Morphology and voxel intensity along the profile were analyzed and suspicious locations were labeled as lesion candidates. The candidates were segmented and 20 morphological features were extracted from each candidate. A data set of 35 patients with 45 biopsy-proven bladder lesions within the C region was used for system evaluation. Stepwise feature selection with simplex optimization and leave-one-case-out method was used for training and validation. For each partition in the leave-one-case-out method, features were selected from the training cases and a linear discriminant (LDA) classifier was designed to merge the selected features into a single score for classification of the lesion candidates into bladder lesions and normal findings in the left-out case. A single score was generated for each lesion candidate. The performance of the CAD system was evaluated by FROC analysis. At an FP rate of 2.5 FPs/case, the system achieved a sensitivity of 82%, while at 1.7 FPs/case, a sensitivity of 71%.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenny Cha, Lubomir Hadjiiski, Heang-Ping Chan, Elaine M. Caoili M.D., Richard H. Cohan M.D., and Chuan Zhou "Computer-aided detection of bladder mass within contrast-enhanced region of CTU", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141Q (20 March 2015);

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