title: Detection of urinary bladder mass in CT urography with SPAN.
abstract: OBJECTIVE
  The authors are developing a computer-aided detection system for bladder cancer on CT urography (CTU). In this study, the authors focused on developing a system for detecting masses fully or partially within the contrast-enhanced (C) region of the bladder.
  
  
  METHODS
  With IRB approval, a data set of 70 patients with biopsy-proven bladder lesions fully or partially immersed within the contrast-enhanced region (C region) of the bladder was collected for this study: 35 patients for the training set (39 malignant, 7 benign lesions) and 35 patients for the test set (49 malignant, 4 benign lesions). The bladder in the CTU images was automatically segmented using the authors' conjoint level set analysis and segmentation system, which they developed specifically to segment the bladder. A closed contour of the C region of the bladder was generated by maximum intensity projection using the property that the dependently layering contrast material in the bladder will be filled consistently to the same level along all CTU slices due to gravity. Potential lesion candidates within the C region contour were found using the authors' Straightened Periphery ANalysis (SPAN) method. SPAN transforms a bladder wall to a straightened thickness profile, marks suspicious pixels on the profile, and clusters them into regions of interest to identify potential lesion candidates. The candidate regions were automatically segmented using the authors' autoinitialized cascaded level set segmentation method. Twenty-three morphological features were automatically extracted from the segmented lesions. The training set was used to determine the best subset of these features using simplex optimization with the leave-one-out case method. A linear discriminant classifier was designed for the classification of bladder lesions and false positives. The detection performance was evaluated on the independent test set by free-response receiver operating characteristic analysis.
  
  
  RESULTS
  At the prescreening step, the authors' system achieved 84.4% sensitivity with an average of 4.3 false positives per case (FPs/case) for the training set, and 84.9% sensitivity with 5.4 FPs/case for the test set. After linear discriminant analysis (LDA) classification with the selected features, the FP rate improved to 2.5 FPs/case for the training set, and 4.3 FPs/case for the test set without missing additional true lesions. By varying the threshold for the LDA scores, at 2.5 FPs/case, the sensitivities were 84.4% and 81.1% for the training and test sets, respectively. At 1.7 FPs/case, the sensitivities decreased to 77.8% and 75.5%, respectively.
  
  
  CONCLUSIONS
  The results demonstrate the feasibility of the authors' method for detection of bladder lesions fully or partially immersed in the contrast-enhanced region of CTU.
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Kenny
authors.initials: K
authors.lastname: Cha
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Richard H
authors.initials: RH
authors.lastname: Cohan
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
mesh_terms: D005240:Feasibility Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D001743:Urinary Bladder / Q000000981:diagnostic imaging; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D014567:Urography / Q000379:methods*