Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography

Acad Radiol. 2005 Jun;12(6):695-707. doi: 10.1016/j.acra.2004.12.026.

Abstract

Rationale and objectives: Radiologists often compare the supine and prone data sets of a patient to confirm potential polyp findings in computed tomographic (CT) colonography (CTC). We developed a new automated method that uses region-based supine-prone correspondence for the reduction of false-positive (FP) polyp candidates in computer-aided detection (CAD) for CTC.

Materials and methods: Up to six anatomic landmarks are established by use of the extracted region of the colonic lumen. A region-growing scheme with distance calculations is used to divide the colonic lumen into overlapping segments that match in the supine and prone data sets. Polyp candidates detected by means of a CAD scheme are eliminated in colonic segments that have sufficient diagnostic quality and contain polyp candidates in only one of the data sets of a patient. The method was evaluated with 121 CTC cases, including 42 polyps of 5 mm or greater in 28 patients, obtained by use of single- and multidetector CT scanners with standard pre-colonoscopy cleansing.

Results: Complete or partial correspondence was established in 71% of cases. Based on a leave-one-patient-out evaluation, application of the method reduced 19% of FP results reported by our CAD scheme at a 90.5% by-polyp detection sensitivity, without loss of any true-positive results. The resulting CAD scheme yielded 2.4 FP results per patient, on average, with the use of the correspondence method, whereas it yielded 3.0 FP results per patient without the use of the method.

Conclusion: The correspondence method is potentially useful for improving the specificity of CAD in CTC.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Colonic Polyps / diagnostic imaging*
  • Colonography, Computed Tomographic / methods*
  • False Positive Reactions
  • Humans
  • Pattern Recognition, Automated
  • Prone Position / physiology*
  • Radiographic Image Interpretation, Computer-Assisted*
  • Retrospective Studies
  • Sensitivity and Specificity
  • Supine Position / physiology*