title: Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography.
abstract: OBJECTIVE
  To develop a computerized system for segmentation of bladder lesions on computed tomography urography (CTU) scans for detection and characterization of bladder cancer.
  
  
  METHODS
  We have developed an auto-initialized cascaded level set method to perform bladder lesion segmentation. The segmentation performance was evaluated on a preliminary dataset including 28 CTU scans from 28 patients collected retrospectively with institutional review board approval. The bladders were partially filled with intravenous contrast material. The lesions were located fully or partially within the contrast-enhanced area or in the non-contrast-enhanced area of the bladder. An experienced abdominal radiologist marked 28 lesions (14 malignant and 14 benign) with bounding boxes that served as input to the automated segmentation system and assigned a difficulty rating on a scale of 1 to 5 (5 = most subtle) to each lesion. The contours from automated segmentation were compared to three-dimensional contours manually drawn by the radiologist. Three performance metric measures were used for comparison. In addition, the automated segmentation quality was assessed by an expert panel of two experienced radiologists, who provided quality ratings of the contours on a scale from 1 to 10 (10 = excellent).
  
  
  RESULTS
  The average volume intersection ratio, the average absolute volume error, and the average distance measure were 67.2 ± 16.9%, 27.3 ± 26.9%, and 2.89 ± 1.69 mm, respectively. Of the 28 segmentations, 18 were given quality ratings of 8 or above. The average rating was 7.9 ± 1.5. The average quality ratings for lesions with difficulty ratings of 1, 2, 3, and 4 were 8.8 ± 0.9, 7.9 ± 1.8, 7.4 ± 0.9, and 6.6 ± 1.5, respectively.
  
  
  CONCLUSIONS
  Our preliminary study demonstrates the feasibility of using the three-dimensional level set method for segmenting bladder lesions in CTU scans.
authors.affiliation: Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5842, USA. lhadjisk@umich.edu
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.forename: Richard H
authors.initials: RH
authors.lastname: Cohan
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D001749:Urinary Bladder Neoplasms / Q000000981:diagnostic imaging*; D014567:Urography / Q000379:methods*