title: Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection.
abstract: OBJECTIVE
  To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.
  
  
  METHODS
  An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.
  
  
  RESULTS
  The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.
  
  
  CONCLUSIONS
  An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892, USA. mglinguraru@gmail.com
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
authors.forename: Neil
authors.initials: N
authors.lastname: Panjwani
authors.forename: Joel G
authors.initials: JG
authors.lastname: Fletcher
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
mesh_terms: D000465:Algorithms; D003106:Colon / Q000000981:diagnostic imaging*; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D006801:Humans; D054368:Laxatives; 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
chemical_list: D054368:Laxatives