title: Computer-aided detection of colonic polyps at CT colonography using a Hessian matrix-based algorithm: preliminary study.
abstract: OBJECTIVE
  The purpose of our study was to develop a Hessian matrix-based computer-aided detection (CAD) algorithm for polyp detection on CT colonography (CTC) and to analyze its performance in a high-risk population.
  
  
  METHODS
  The CTC data sets of 35 patients with at least one colonoscopically proven polyp were interpreted with a Hessian matrix-based CAD algorithm, which was designed to depict bloblike structures protruding into the lumen. Our gold standard was a combination of segmental unblinded optical colonoscopy and retrospective unblinded consensus review by two radiologists. Sensitivity of CAD for polyp detection was evaluated on both per-polyp and per-patient bases. The average number of false-positive detections was calculated, and the causes of false-positives and false-negatives were analyzed.
  
  
  RESULTS
  Ninety-four polyps were identified on colonoscopy. Forty-six polyps were smaller than 6 mm and 48 were 6 mm or larger. Seventy-five (79.8%) of these 94 polyps were identified by radiologists in a retrospective review. When colonoscopy was used as a standard of reference, the sensitivity of CAD was 77.1% for polyps 6 mm or larger. For large polyps (> or = 6 mm) that could be identified on retrospective review, the CAD algorithm achieved sensitivities of 92.5% (37/40) and 91.7% (22/24), respectively, on per-polyp and per-patient bases. There were an average of 5.5 false-positive detections per patient and 3.1 false-positive detections per data set for CAD. The two most frequent causes of false-positives on CAD were prominent or converging fold (78/191) and feces (50/191). Of the three polyps 6 mm or larger that were missed by CAD, two had a flat appearance on colonoscopy and the remaining one was located in the narrow area between the rectal tube and the rectal wall.
  
  
  CONCLUSIONS
  A Hessian matrix-based CAD algorithm for CTC has the potential to depict polyps larger than or equal to 6 mm with high sensitivity and an acceptable false-positive rate.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul 110-744, Korea.
authors.forename: Se Hyung
authors.initials: SH
authors.lastname: Kim
authors.forename: Jeong Min
authors.initials: JM
authors.lastname: Lee
authors.forename: Joon-Goo
authors.initials: JG
authors.lastname: Lee
authors.forename: Jong Hyo
authors.initials: JH
authors.lastname: Kim
authors.forename: Philippe A
authors.initials: PA
authors.lastname: Lefere
authors.forename: Joon Koo
authors.initials: JK
authors.lastname: Han
authors.forename: Byung Ihn
authors.initials: BI
authors.lastname: Choi
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity